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Record W2914514848 · doi:10.15694/mep.2019.000027.1

The Crisis of Health Professions Education in Pakistan

2019· article· en· W2914514848 on OpenAlexafffund
Arslaan Javaeed

Bibliographic record

VenueMedEdPublish · 2019
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsHealth professionsPolitical scienceMedicineDevelopment economicsEconomic growthHealth careEconomics

Abstract

fetched live from OpenAlex

This article was migrated. The article was marked as recommended. Health professions are critical in maintaining the health infrastructure of the country. The quality of health care education imparted directly affects the people and it is thus very important to focus our attention towards it. This paper is focused on the current dilemmas being faced in the health professions education and a two-step solution was proposed in view of the deep understanding of the problem. The first step includes a teacher training course to educate and empower the educator about the current advancements in the healthcare professions education. The second step included a thorough evaluation of medical educators to assess their teaching practices and bring forward solutions to improve them. Undoubtedly it can be stated that there is a dire need to implement principles of health professions education in Pakistan and educators have to play their crucial role in understanding the current challenges and taking steps to overcome them.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.054
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0050.003
Open science0.0000.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0110.001

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 head0.017
GPT teacher head0.410
Teacher spread0.393 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations9
Published2019
Admission routes2
Has abstractyes

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