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Record W2966718002 · doi:10.4103/bjhs.bjhs_45_18

Moving toward competency-based medical education

2018· article· en· W2966718002 on OpenAlexaboutno aff
Tejaswini Vallabha

Bibliographic record

VenueBLDE University Journal of Health Sciences · 2018
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationPsychologyMedicine

Abstract

fetched live from OpenAlex

Competency-based medical education (CBME) has been adopted by many countries over the last two decades. Medical councils throughout the world are switching over to CBME. Though late, the regulatory body for medical education in India has moved toward CBME. The recent announcement by the Medical Council of India (MCI) regarding the implementation of CBME for UG and PG curricula from the next academic sessions is a major shift in the policy. The concept of CBME started in 1950 when Tyler[1] proposed that the education should be outcome based. Prof. Robert Carroll[2] brought in this concept without using the word competence. Later, an extensive description regarding CBME in comparison with the existing curricular methods was given by McGaghie et al.[3] in 1978. Attempts to correlate course contents, teaching–learning, and outcomes were made by various groups of medical educators randomly. By the end of the 20th century, the need for CBME was crystallized, and the Canadian Medical Council brought out Can Meds Competency Framework in 2004,[4] broadly covering the competencies in six domains to be a medical expert. Similarly, the Accreditation Council for Graduate Medical Education[5] described the expected outcomes in six domains. Over the years, these are refined, modified, and adopted by the medical schools all over the world. McGaghie et al.[3] described how the CBME differs from the subject-centered curriculum and integrated curriculum in three fundamental ways: “Curriculum is organized around functions or competencies required for the practice of medicine in a particular setting It is grounded on empirically validated principle that students of intellectual quality found in the medical schools, when given appropriate instruction can master basic performance objectives It views CBME as an experiment where both processes of learning and technique of learning are considered as hypothesis subject to testing.” The expected outcome would be a physician who can practice medicine at a predetermined level of proficiency as per the needs. The existing curriculum practiced in India is a subject-centric model with attempts of patchy integration. Realizing the limitations and gaps in the outcomes, the MCI has moved to CBME. The major components of CBME are clearly spelled objectives of the program with appropriate teaching–learning methods and methodology of assessment. The curricula of postgraduate degree and diplomas released in March 2018 reflect the efforts toward defining outcomes.[6] The national goals are clearly specific and explicit. Similarly, subject-specific goals and objectives have spelled out the expected outcomes and divided these under various domains. Significant customizations as per the needs of the particular specialization are also attempted within the general regulations and broad framework. As explained in the preamble, the reconciliation team focused mainly on the outcomes and alignment of the rest of the course content compromising grammar and syntax with sole purpose to simplify and reach all the stakeholders for easy implementation.[6] The MCI recently uploaded the curriculum for MBBS.[7] These documents, prepared with significant efforts by the team, have made major changes. The outcomes are clear and explicit with appropriate alignment to core objectives toward producing competent Indian Medical Graduate. It is interesting to note that the document explicitly suggests teaching–learning methods and assessment and addresses vertical and horizontal integration unambiguously. Details of the regulatory component are awaited, which will spell out the necessary information on quantitative implementation-related issues. At this juncture, it is appropriate to recollect and recognize the efforts made by the Rajiv Gandhi University of Health Sciences in the early 2000s, under the leadership of Former Director of Curriculum Development Prof. D. K. Shrinivas. He developed goals, objectives under various domains and suggested T-L methods, guidelines for monitoring progress when majority of universities in India had vague ideas. Implementation of PG curricula is comparatively easier as many universities have similar curricula, but need effective implementation. However, the situation may not be same for the implementation of MBBS curriculum. Although there have been continuous efforts toward sensitizing and training faculty for 4–5 years by the medical education units [MEUs], regional centers, and nodal centers across the country, there is skepticism regarding trickling of concepts at all levels. Learning is probably patchy and more of methodology oriented in general. There will be a challenge for governance to ensure appropriate implementation and effective monitoring of teaching-learning activities and aligning the schedule with existing batches who are receiving a traditional method of training. Grassroots level understanding and coordination remain key methods. Faculty deficit and addressing untrained or reluctant faculty can be a majorhurdle in implementation. The role of enthusiastic, influential, and knowledgeable proactive MEUs in every institute will be invaluable in bringing about change smoothly during this transition period. Ineffective half-hearted implementation can be disastrous, and CBME will be looked upon as an idea with a piecemeal approach. Good supportive governance and provision of needs including adequate faculty will be the cornerstone. Timely assistance for implementation from the MCI is a must. Known for its rigid, nonflexible approach toward accreditation and processes, the MCI should now focus on quality enhancement, rather than blindly following rigid irrelevant assessment methods of medical schools.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.361
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2018
Admission routes1
Has abstractyes

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