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Record W4391171421 · doi:10.3329/cbmj.v13i1.71097

Community Based Medical Education: What, Why and How?

2024· article· en· W4391171421 on OpenAlexaff
Abu Sadat Mohammad Nurunnabi, Mahmud Javed Hasan, ASM Ruhul Quddush, Amir Mohammad Kaiser, Tanzina Afrose, Shamima Parveen

Bibliographic record

VenueCommunity Based Medical Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsMedical educationSociologyComputer scienceData scienceMedicine

Abstract

fetched live from OpenAlex

Community based medical education has several definitions, but the core definition refers to learning that takes place is need based and in a community setting. Hence, community based medical education refers to medical education in which trainees learn and acquire professional competencies in a community setting based on the need of the community. This concept encourages medical colleges to produce not just highly competent professionals, but professionals who are equipped to respond to the changing challenges of healthcare through re-orientation of their education, research, and service commitments, and be capable of demonstrating a positive effect upon the communities they serve. Such social accountability of a healthcare cum academic institution demonstrates an impact on the communities served and thus, contribute to achieve a just and efficient healthcare service through mutually beneficial partnerships with other stakeholders. Community based medical education can make a difference in the country’s health sector by supporting a community based healthcare delivery system within the concept of National Health Policy and thus, contribute to the overall national efforts in achieving meaningful, self-sustaining quality of life and environment. Besides, it helps bring about change in current educational trait by imposing need-based, flexible academic strategies specific to the rural community and quality of the medical doctors by grooming them as empathetically responsive and active towards patients, professionally competent and ethically sound persons of the society. CBMJ 2024 January: vol. 13 no. 01 P: 119-129

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)0.013
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.539
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.013
Insufficient payload (model declined to judge)0.0050.000

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 head0.045
GPT teacher head0.378
Teacher spread0.333 · 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.

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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