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Record W3087442866

Social Accountability and Accreditation: Impacting Health System Performance and Population Health

2020· article· en· W3087442866 on OpenAlexaff
Titi Savitri Prihatiningsih, Yassein Kamal, Robert Woollard, Julian Fisher, Mohamed Elhassan Abdalla, Charles Boelen

Bibliographic record

VenueSocial Innovations Journal · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAccountabilityAccreditationPublic relationsSocial accountingPolitical scienceGeneral partnershipSocial determinants of healthHealth careBusinessAccounting
DOInot available

Abstract

fetched live from OpenAlex

Today’s reality all over the world has shown a huge disparity in the quality, equity, relevance, partnership and efficiency in the provision of health services resulting in huge gap of health status in many societies across the globe, be it in the developed and the developing countries. A number of reasons have been discussed. One most important reason is the disconnected between medical schools and health profession education institutions with their ecosystem and community they are mandated to serve. The concept of social accountability endorsed by the WHO since 1995 has not really been embraced by medical and health profession education institutions and not yet supported by key policy makers and health managers in many regions and countries. A few case studies have proved that the concept of social accountability is feasible and managable; and it eventually brings beneficial impact for the society in improving the health status. Existing guidelines and approaches  could be used to accelerate the adoption of social accountability as long as key actors from international, national, institution and community levels are orchestrated congruently. A new paradigm in school’s accreditation embracing social accountability concept could reinforce this venture.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0080.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.266
GPT teacher head0.527
Teacher spread0.261 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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