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The Telephone Helpline of Persian Medicine: Social Accountability During the COVID-19 Pandemic

2021· article· en· W4200241908 on OpenAlexaboutno aff
Fatemeh Eghbalian, Somayeh Delavari, Hoorieh Mohammadi Kenari

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Sanctions and International Relations
Canadian institutionsnot available
Fundersnot available
KeywordsPersianHelplinePandemicCoronavirus disease 2019 (COVID-19)Accountability2019-20 coronavirus outbreakPolitical scienceMedicineVirologyLawEmergency medicineInternal medicinePhilosophy

Abstract

fetched live from OpenAlex

Social accountability serves as an essential factor in improving the quality, efficiency, and responsiveness of health systems (1). Health and medical education policy-makers emphasize social accountability as a measure of medical universities’ commitment with regard to community health priorities (2). In 1995 the World Health Organization (WHO) defined social accountability as: “The obligation of the medical schools to direct their education, research and/or service activities towards addressing the priority health concerns of the community, region, and/or nation they have the mandate to serve. Priority health concerns are to be jointly identified by governments, health care organisations, health professionals, and the public”(3). Social accountability principles oblige education policy-makers to consider costeffectiveness, quality, equity, and relevance in planning, delivering, and evaluation of educational programs, services and research activities (2). Social accountability in medical curriculums would fulfill the target community’s requirements in the health system (4). The Association of Faculties of Medicine of Canada (AFMC) and the Global Consensus for Social Accountability of Medical Schools (GCSA) have emphasized that every medical university’s mission should be based on linking medical education systems with community health requirements.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.000

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 head0.369
GPT teacher head0.532
Teacher spread0.163 · 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 designObservational
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

Citations0
Published2021
Admission routes1
Has abstractyes

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