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Fulltext The implementation of problem-based learning in collaborative groups in a chiropractic program in Malaysia

Win NN, Nadarajah VD, Win DK

J Educ Eval Health Prof, 2015;12:17.
PMID: 25961676 DOI: 10.3352/jeehp.2015.12.17

PURPOSE: Problem-based learning (PBL) is usually conducted in small-group learning sessions with approximately eight students per facilitator. In this study, we implemented a modified version of PBL involving collaborative groups in an undergraduate chiropractic program and assessed its pedagogical effectiveness.
METHODS: This study was conducted at the International Medical University, Kuala Lumpur, Malaysia, and involved the 2012 chiropractic student cohort. Six PBL cases were provided to chiropractic students, consisting of three PBL cases for which learning resources were provided and another three PBL cases for which learning resources were not provided. Group discussions were not continuously supervised, since only one facilitator was present. The students' perceptions of PBL in collaborative groups were assessed with a questionnaire that was divided into three domains: motivation, cognitive skills, and perceived pressure to work.
RESULTS: Thirty of the 31 students (97%) participated in the study. PBL in collaborative groups was significantly associated with positive responses regarding students' motivation, cognitive skills, and perceived pressure to work (P<0.05). The students felt that PBL with learning resources increased motivation and cognitive skills (P<0.001).
CONCLUSION: The new PBL implementation described in this study does not require additional instructors or any additional funding. When implemented in a classroom setting, it has pedagogical benefits equivalent to those of small-group sessions. Our findings also suggest that students rely significantly on available learning resources.
KEYWORDS: Chiropractic; Learning; Motivation; Perception; Problem-based learning

Matched MeSH terms: Problem-Based Learning*
Hierarchical gated recurrent neural network with adversarial and virtual adversarial training on text classification

Poon HK, Yap WS, Tee YK, Lee WK, Goi BM

Neural Netw, 2019 Nov;119:299-312.
PMID: 31499354 DOI: 10.1016/j.neunet.2019.08.017

Document classification aims to assign one or more classes to a document for ease of management by understanding the content of a document. Hierarchical attention network (HAN) has been showed effective to classify documents that are ambiguous. HAN parses information-intense documents into slices (i.e., words and sentences) such that each slice can be learned separately and in parallel before assigning the classes. However, introducing hierarchical attention approach leads to the redundancy of training parameters which is prone to overfitting. To mitigate the concern of overfitting, we propose a variant of hierarchical attention network using adversarial and virtual adversarial perturbations in 1) word representation, 2) sentence representation and 3) both word and sentence representations. The proposed variant is tested on eight publicly available datasets. The results show that the proposed variant outperforms the hierarchical attention network with and without using random perturbation. More importantly, the proposed variant achieves state-of-the-art performance on multiple benchmark datasets. Visualizations and analysis are provided to show that perturbation can effectively alleviate the overfitting issue and improve the performance of hierarchical attention network.

Matched MeSH terms: Machine Learning*
Fulltext DeepMoney: counterfeit money detection using generative adversarial networks

Ali T, Jan S, Alkhodre A, Nauman M, Amin M, Siddiqui MS

PeerJ Comput Sci, 2019;5:e216.
PMID: 33816869 DOI: 10.7717/peerj-cs.216

Conventional paper currency and modern electronic currency are two important modes of transactions. In several parts of the world, conventional methodology has clear precedence over its electronic counterpart. However, the identification of forged currency paper notes is now becoming an increasingly crucial problem because of the new and improved tactics employed by counterfeiters. In this paper, a machine assisted system-dubbed DeepMoney-is proposed which has been developed to discriminate fake notes from genuine ones. For this purpose, state-of-the-art models of machine learning called Generative Adversarial Networks (GANs) are employed. GANs use unsupervised learning to train a model that can then be used to perform supervised predictions. This flexibility provides the best of both worlds by allowing unlabelled data to be trained on whilst still making concrete predictions. This technique was applied to Pakistani banknotes. State-of-the-art image processing and feature recognition techniques were used to design the overall approach of a valid input. Augmented samples of images were used in the experiments which show that a high-precision machine can be developed to recognize genuine paper money. An accuracy of 80% has been achieved. The code is available as an open source to allow others to reproduce and build upon the efforts already made.

Matched MeSH terms: Machine Learning; Unsupervised Machine Learning
Personality traits and learning

Kohli S, Bhatia S

Br Dent J, 2021 02;230(4):186.
PMID: 33637900 DOI: 10.1038/s41415-021-2752-2
Matched MeSH terms: Learning*
Fulltext An Improved Vulnerability Exploitation Prediction Model with Novel Cost Function and Custom Trained Word Vector Embedding

Hoque MS, Jamil N, Amin N, Lam KY

Sensors (Basel), 2021 Jun 20;21(12).
PMID: 34202977 DOI: 10.3390/s21124220

Successful cyber-attacks are caused by the exploitation of some vulnerabilities in the software and/or hardware that exist in systems deployed in premises or the cloud. Although hundreds of vulnerabilities are discovered every year, only a small fraction of them actually become exploited, thereby there exists a severe class imbalance between the number of exploited and non-exploited vulnerabilities. The open source national vulnerability database, the largest repository to index and maintain all known vulnerabilities, assigns a unique identifier to each vulnerability. Each registered vulnerability also gets a severity score based on the impact it might inflict upon if compromised. Recent research works showed that the cvss score is not the only factor to select a vulnerability for exploitation, and other attributes in the national vulnerability database can be effectively utilized as predictive feature to predict the most exploitable vulnerabilities. Since cybersecurity management is highly resource savvy, organizations such as cloud systems will benefit when the most likely exploitable vulnerabilities that exist in their system software or hardware can be predicted with as much accuracy and reliability as possible, to best utilize the available resources to fix those first. Various existing research works have developed vulnerability exploitation prediction models by addressing the existing class imbalance based on algorithmic and artificial data resampling techniques but still suffer greatly from the overfitting problem to the major class rendering them practically unreliable. In this research, we have designed a novel cost function feature to address the existing class imbalance. We also have utilized the available large text corpus in the extracted dataset to develop a custom-trained word vector that can better capture the context of the local text data for utilization as an embedded layer in neural networks. Our developed vulnerability exploitation prediction models powered by a novel cost function and custom-trained word vector have achieved very high overall performance metrics for accuracy, precision, recall, F1-Score and AUC score with values of 0.92, 0.89, 0.98, 0.94 and 0.97, respectively, thereby outperforming any existing models while successfully overcoming the existing overfitting problem for class imbalance.

Matched MeSH terms: Machine Learning*
Fulltext Physiology without borders: report on physiology education workshops in India-IUPS Initiatives (2018-2019)

Chandran DS, Muthukrishnan SP, Barman SM, Peltonen LM, Ghosh S, Sharma R, et al.

Adv Physiol Educ, 2020 09 01;44(3):309-313.
PMID: 32484399 DOI: 10.1152/advan.00050.2020
Matched MeSH terms: Problem-Based Learning*
Fulltext Ridge regression and its applications in genetic studies

Arashi M, Roozbeh M, Hamzah NA, Gasparini M

PLoS One, 2021;16(4):e0245376.
PMID: 33831027 DOI: 10.1371/journal.pone.0245376

With the advancement of technology, analysis of large-scale data of gene expression is feasible and has become very popular in the era of machine learning. This paper develops an improved ridge approach for the genome regression modeling. When multicollinearity exists in the data set with outliers, we consider a robust ridge estimator, namely the rank ridge regression estimator, for parameter estimation and prediction. On the other hand, the efficiency of the rank ridge regression estimator is highly dependent on the ridge parameter. In general, it is difficult to provide a satisfactory answer about the selection for the ridge parameter. Because of the good properties of generalized cross validation (GCV) and its simplicity, we use it to choose the optimum value of the ridge parameter. The GCV function creates a balance between the precision of the estimators and the bias caused by the ridge estimation. It behaves like an improved estimator of risk and can be used when the number of explanatory variables is larger than the sample size in high-dimensional problems. Finally, some numerical illustrations are given to support our findings.

Matched MeSH terms: Machine Learning*
Fulltext Applying the learning theories to medical education: A commentary

Sivalingam Nalliah, Nazimah Idris

International e-Journal of Science, Medicine & Education, 2014;8(1):50-57.
MyJurnal

Medical education of today continues to evolve to meet the challenges of the stakeholders. Medical professionals today are expected to
play multiple roles besides being experts. Thus, the curriculum has to be developed in a manner that facilitates learners to achieve the intended goal of becoming a medical professional with multiple competencies. The understanding of learning theories will be helpful in designing and delivering the curriculum to meet the demands of producing a medical professional who would meet the CanMEDS model.
This commentary explores and reflects on the learning theories of behaviorism, cognitivism and constructivism as they have evolved over time and the application of these learning theories in medical education, particularly in the context of medical education in Malaysia. The authors are convinced that these three theories are not mutually exclusive but should be operationalized contextually and throughout the
different stages of learning in the MBBS curriculum. Understanding these theories and their application will enhance the learning experience of students.

Matched MeSH terms: Learning; Problem-Based Learning
Fulltext Learning through teaching and sharing in the Jigsaw Classroom

Ab Murat, N.

Ann Dent, 2008;15(2):71-76.
MyJurnal

Teaching is a complex activity which consists not only of giving instructions but also promotion of learning. Different students have different preference for learning styles. Dental educators must therefore attempt to mix and match their methods of teaching to accommodate students with differing learning styles to provide an opportunity to maximize their learning. This paper aims to share the writer's experience and students' perceptions towards a different mode of teaching/learning method. The Jigsaw Classroom method was employed on University of Malaya's third-year dental students during their Water Fluoridation lecture. At the end of the session, students were asked to reflect upon the learning experience and to inscribe their feelings. Initially, students showed their resentment towards the new learning style but their resistance changed once they got into a group and started to learn from each other. In the reflective essay, most students expressed that learning through teaching and discussing as required in the Jigsaw method enhanced their understanding of the topic and they claimed that they were able to retain the information better. In this study, the Jigsaw method proved that learning in the lecture hall can be fun, educational and enriching.

Matched MeSH terms: Learning; Problem-Based Learning
Fulltext The naqli and ‘aqli integration in the teachings of community health posting

Dzulhairi, M.R., Zarina, A.R., Nooriah, M.S., Yunus, M.

International Medical Journal Malaysia, 2015;14(1):83-86.
MyJurnal

The Community Health Posting teaching module is incorporated in the fourth year medical curriculum at Universiti Sains Islam Malaysia (USIM). The integration of Islamic principles and values in the medical curriculum is emphasized during the Community Health Posting. The Community Health curriculum allow students to appreciate and understand the medical and fiqh aspects of health and disease, the social issues in medical practice and research and to inculcate the practice of Islamic professional etiquettes. The teaching module illustrates the relevance of humanities in understanding illness and medical care within the community. Teaching and learning activities include components that enable the students to explore a wide range of influencing factors and how these affect the patients and their families. Issues pertaining to psychosocial and ecological perspectives of the community are also discussed. This posting utilizes various teaching and learning techniques such as lectures, tutorials, seminars, group discussions, educational visits, practical sessions and patient bedside teaching. In addition, the students are equipped with Islamic knowledge through the integration of Naqli and Aqli components in the Community Health Posting curriculum.

Matched MeSH terms: Learning; Problem-Based Learning
A survey of recently emerged genome-wide computational enhancer predictor tools

Lim LWK, Chung HH, Chong YL, Lee NK

Comput Biol Chem, 2018 Jun;74:132-141.
PMID: 29602043 DOI: 10.1016/j.compbiolchem.2018.03.019

The race for the discovery of enhancers at a genome-wide scale has been on since the commencement of next generation sequencing decades after the discovery of the first enhancer, SV40. A few enhancer-predicting features such as chromatin feature, histone modifications and sequence feature had been implemented with varying success rates. However, to date, there is no consensus yet on the single enhancer marker that can be employed to ultimately distinguish and uncover enhancers from the enormous genomic regions. Many supervised, unsupervised and semi-supervised computational approaches had emerged to complement and facilitate experimental approaches in enhancer discovery. In this review, we placed our focus on the recently emerged enhancer predictor tools that work on general enhancer features such as sequences, chromatin states and histone modifications, eRNA and of multiple feature approach. Comparisons of their prediction methods and outcomes were done across their functionally similar counterparts. We provide some recommendations and insights for future development of more comprehensive and robust tools.

Matched MeSH terms: Machine Learning*
Fulltext Explanation identity of basic programming and conceptual change through the game scene

May Asliza Tan Zalilah, Maizatul Hayati Mohamad Yatim, Amri Yusoff

Journal of Engineering and Science Research, 2017;1(2):93-100.
MyJurnal

Learning through game scene is considered a game-based learning approach. Teaching and learning process using game scene is deemed interesting and effective due to the nature for this approach which seems alive with asserted activities. Students experience their own game via narration through the virtual world they undertook. This investigation is targeted towards conceptual change and explanation for basic programming theorem through navigated game scene by evaluating motivation and student experience. 55 respondents consisted of semester three students from computer software application certification a program from a community college is selected for the undertaken study. Motivation and experience surveys are reference based on intrinsic motivation inventory instrument (IMI). Findings were tabulated based on t-test statistics and descriptive to get the frequency, mean, standard deviation and percentage. Initial results reflected student acknowledgement on utilizing game scenes in terms of elaborating basic game programming key points in providing elevated learning experience.

Matched MeSH terms: Learning; Problem-Based Learning
Fulltext An improved adaptive memetic differential evolution optimization algorithms for data clustering problems

Mustafa HMJ, Ayob M, Nazri MZA, Kendall G

PLoS One, 2019;14(5):e0216906.
PMID: 31137034 DOI: 10.1371/journal.pone.0216906

The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process. Although some data clustering algorithms have achieved reasonable quality solutions for some datasets, their performance across real-life datasets could be improved. This paper proposes an adaptive memetic differential evolution optimisation algorithm (AMADE) for addressing data clustering problems. The memetic algorithm (MA) employs an adaptive differential evolution (DE) mutation strategy, which can offer superior mutation performance across many combinatorial and continuous problem domains. By hybridising an adaptive DE mutation operator with the MA, we propose that it can lead to faster convergence and better balance the exploration and exploitation of the search. We would also expect that the performance of AMADE to be better than MA and DE if executed separately. Our experimental results, based on several real-life benchmark datasets, shows that AMADE outperformed other compared clustering algorithms when compared using statistical analysis. We conclude that the hybridisation of MA and the adaptive DE is a suitable approach for addressing data clustering problems and can improve the balance between global exploration and local exploitation of the optimisation algorithm.

Matched MeSH terms: Machine Learning*
Problem-based learning (PBL): making an impact in education

Azila NMA, Sim SM, Tan CPL, Alhady SF

JUMMEC, 1999;4<I> </I>:94-98.

Problem-based learning (PBL) i s an educational reform that is now becoming a household word in higher education, particularly in medical schools. Many medical schools have implemented a full problem-based learning curriculum (PBLC) whiIe some have included PBL into selected units of the course in an otherwise conventional cumculum (embedded PBL) and others run their tutorials in a PBL manner within a modified conventional curriculum (hybrid curriculum). Yet there are others who claim that small components of PBL in a conventional curriculum are not PBL at all. Thus amateurs in the subject matter find difficulty in evaluating the logistics and outcome of these variations. This article focuses or, the general characteristics of PBL and how this learning method can help enhance independent learning and critical thinking, whether in a full, embedded or hybrid curriculum. The extent of PBL to be included and which of the three types is to be adopted depends on the objective of the undergraduate medical course as determined by the faculty, resources available, limitations, feedback on the existing curriculum and various other factors. KEYWORDS: Problem-based Learning (PBL); Embedded PBL; Hybrid PBL; New Integrated Curriculum (NIC).

Matched MeSH terms: Learning; Problem-Based Learning
Fulltext A static analysis approach for Android permission-based malware detection systems

Mohamad Arif J, Ab Razak MF, Awang S, Tuan Mat SR, Ismail NSN, Firdaus A

PLoS One, 2021;16(9):e0257968.
PMID: 34591930 DOI: 10.1371/journal.pone.0257968

The evolution of malware is causing mobile devices to crash with increasing frequency. Therefore, adequate security evaluations that detect Android malware are crucial. Two techniques can be used in this regard: Static analysis, which meticulously examines the full codes of applications, and dynamic analysis, which monitors malware behaviour. While both perform security evaluations successfully, there is still room for improvement. The goal of this research is to examine the effectiveness of static analysis to detect Android malware by using permission-based features. This study proposes machine learning with different sets of classifiers was used to evaluate Android malware detection. The feature selection method in this study was applied to determine which features were most capable of distinguishing malware. A total of 5,000 Drebin malware samples and 5,000 Androzoo benign samples were utilised. The performances of the different sets of classifiers were then compared. The results indicated that with a TPR value of 91.6%, the Random Forest algorithm achieved the highest level of accuracy in malware detection.

Matched MeSH terms: Machine Learning*
Fulltext A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos

Aghamohammadi A, Ang MC, A Sundararajan E, Weng Ng K, Mogharrebi M, Banihashem SY

PLoS One, 2018;13(2):e0192246.
PMID: 29438421 DOI: 10.1371/journal.pone.0192246

Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods.

Matched MeSH terms: Learning*
Fulltext Machine learning assisted hepta band THz metamaterial absorber for biomedical applications

Jain P, Chhabra H, Chauhan U, Prakash K, Gupta A, Soliman MS, et al.

Sci Rep, 2023 Jan 31;13(1):1792.
PMID: 36720922 DOI: 10.1038/s41598-023-29024-x

A hepta-band terahertz metamaterial absorber (MMA) with modified dual T-shaped resonators deposited on polyimide is presented for sensing applications. The proposed polarization sensitive MMA is ultra-thin (0.061 λ) and compact (0.21 λ) at its lowest operational frequency, with multiple absorption peaks at 1.89, 4.15, 5.32, 5.84, 7.04, 8.02, and 8.13 THz. The impedance matching theory and electric field distribution are investigated to understand the physical mechanism of hepta-band absorption. The sensing functionality is evaluated using a surrounding medium with a refractive index between 1 and 1.1, resulting in good Quality factor (Q) value of 117. The proposed sensor has the highest sensitivity of 4.72 THz/RIU for glucose detection. Extreme randomized tree (ERT) model is utilized to predict absorptivities for intermediate frequencies with unit cell dimensions, substrate thickness, angle variation, and refractive index values to reduce simulation time. The effectiveness of the ERT model in predicting absorption values is evaluated using the Adjusted R2 score, which is close to 1.0 for nmin = 2, demonstrating the prediction efficiency in various test cases. The experimental results show that 60% of simulation time and resources can be saved by simulating absorber design using the ERT model. The proposed MMA sensor with an ERT model has potential applications in biomedical fields such as bacterial infections, malaria, and other diseases.

Matched MeSH terms: Machine Learning*
Evidence for commonly used teaching, learning and assessment methods in contact lens clinical skills education

Woods C, Naroo S, Zeri F, Bakkar M, Barodawala F, Evans V, et al.

Cont Lens Anterior Eye, 2023 Apr;46(2):101821.
PMID: 36805277 DOI: 10.1016/j.clae.2023.101821

INTRODUCTION: Evidence based practice is now an important part of healthcare education. The aim of this narrative literature review was to determine what evidence exists on the efficacy of commonly used teaching and learning and assessment methods in the realm of contact lens skills education (CLE) in order to provide insights into best practice. A summary of the global regulation and provision of postgraduate learning and continuing professional development in CLE is included.
METHOD: An expert panel of educators was recruited and completed a literature review of current evidence of teaching and learning and assessment methods in healthcare training, with an emphasis on health care, general optometry and CLE.
RESULTS: No direct evidence of benefit of teaching and learning and assessment methods in CLE were found. There was evidence for the benefit of some teaching and learning and assessment methods in other disciplines that could be transferable to CLE and could help students meet the intended learning outcomes. There was evidence that the following teaching and learning methods helped health-care and general optometry students meet the intended learning outcomes; clinical teaching and learning, flipped classrooms, clinical skills videos and clerkships. For assessment these methods were; essays, case presentations, objective structured clinical examinations, self-assessment and formative assessment. There was no evidence that the following teaching and learning methods helped health-care and general optometry students meet the intended learning outcomes; journal clubs and case discussions. Nor was any evidence found for the following assessment methods; multiple-choice questions, oral examinations, objective structured practical examinations, holistic assessment, and summative assessment.
CONCLUSION: Investigation into the efficacy of common teaching and learning and assessment methods in CLE are required and would be beneficial for the entire community of contact lens educators, and other disciplines that wish to adapt this approach of evidence-based teaching.

Matched MeSH terms: Learning*
Fulltext LEARNING CURVE IN ESOPHAGEAL ENDOSCOPIC SUBMUCOSAL DISSECTION BY WESTERN ENDOSCOSPISTS TRAINED IN JAPAN: EXPERIENCE IN LATIN AMERICA

Aliaga Ramos J, Yoshida N, Abdul Rani R, Arantes VN

Arq Gastroenterol, 2023;60(2):208-216.
PMID: 37556747 DOI: 10.1590/S0004-2803.20230222-168

•This study aimed to assess the learning curve effect on patient's clinical outcome for EESD. Retrospective observational study, enrolling patients that underwent EESD from 2009 to 2021, divided in 2 groups. Mean procedure time was 111.8 min and 103.6 min for T1 and T2, respectively (P=0.004). The learning curve in esophageal ESD could be overcomed effectively and safely by an adequately trained Western endoscopist. Background - Esophageal endoscopic submucosal dissection (EESD) is a complex and time-consuming procedure at which training are mainly available in Japan. There is a paucity of data concerning the learning curve to master EESD by Western endoscopists. Objective - This study aimed to assess the learning curve effect on patient's clinical outcome for EESD. Methods - This is a retrospective observational study. Enrolling patients that underwent EESD from 2009 to 2021. The analysis was divided into two periods; T1: case 1 to 49 and T2: case 50 to 98. The following features were analyzed for each group: patients and tumors characteristics, en-bloc, complete and curative resection rates, procedure duration and adverse events rate. Results - Ninety-eight EESD procedures were performed. Mean procedure time was 111.8 min and 103.6 min for T1 and T2, respectively (P=0.004). En bloc resection rate was 93.8% and 97.9% for T1 and T2, respectively (P=0.307). Complete resection rate was 79.5% and 85.7% for T1 and T2, respectively (P=0.424). Curative resection rate was 65.3% and 71.4% for T1 and T2, respectively (P=0.258). Four patients had complications; three during T1 period and one during T2 period. Overall mortality rate: 0%. Conclusion - The esophageal endoscopic submucosal dissection could be performed effectively and safely by an adequately trained Western endoscopist.

Matched MeSH terms: Learning Curve*
Fulltext Measures of ability to learn, grow and make decisions among older persons: a systematic review of measurement properties

Mansor N, Awang H, Amuthavalli Thiyagarajan J, Mikton C, Diaz T

Age Ageing, 2023 Oct 28;52(Suppl 4):iv118-iv132.
PMID: 37902520 DOI: 10.1093/ageing/afad101

OBJECTIVE: this study aims to conduct a systematic review on available instruments for measuring older persons' ability to learn, grow and make decisions and to critically review the measurement properties of the identified instruments.
METHODS: we searched six electronic databases, which include PubMed, Embase, PsycINFO, SciELO, ERIC and AgeLine, between January 2000 and April 2022. Reference lists of the included papers were also manually searched. The COSMIN (CONsensus-based Standards for the selection of health Measurement Instruments) guidelines were used to evaluate the measurement properties and the quality of evidence for each instrument.
RESULTS: 13 instruments from 29 studies were included for evaluation of their measurement properties. Of the 13 reviewed, 6 were on the ability to learn, 3 were on the ability to grow and 4 were on the ability to make decisions. The review found no single instrument that measured all three constructs in unidimensional or multidimensional scales. Many of the instruments were found to have sufficient overall rating on content validity, structural validity, internal consistency and cross-cultural validity. The quality of evidence was rated as low due to a limited number of related validation studies.
CONCLUSION: a few existing instruments to assess the ability to learn, grow and make decisions of older people can be identified in the literature. Further research is needed in validating them against functional, real-world outcomes.

Matched MeSH terms: Learning*

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