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Record W4389064276 · doi:10.4103/efh.efh_285_22

Social Responsiveness: The Key Ingredient to Achieve Social Accountability in Education and Health Care

2023· article· en· W4389064276 on OpenAlexaff
Shakuntala Chhabra, Roger Strasser, Hoi F. Cheu

Bibliographic record

VenueEducation for Health · 2023
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsLaurentian University
Fundersnot available
KeywordsCompassionAccountabilityDutyPublic relationsHealth carePsychologyObligationBureaucracyPolitical scienceSociologyLawPolitics

Abstract

fetched live from OpenAlex

While social accountability (SA) is regarded as an obligation or mandate for medical school administration, it runs the danger of becoming a bureaucratic checkbox. Compassion which leads to social responsiveness (SR), in contrast, is often recognized as an individual characteristic, detached from the public domain. The two, however, complement each other in practice. Institutions must be truly socially accountable, which is possible if there is spontaneous SR to the needs, and is fueled by compassion. Compassion in this article is defined as a "feeling for other people's sufferings, and the desire to act to relieve the suffering." Compassion has a long history, whereas SA is more recently described concept that follows the historical development of social justice. SR is the moral or ethical duty of an individual to behave in a way that benefits society. Not everyone feels the need to do something for others. Even if the need is felt, there may be a lack of will to act for the needs or to act effectively to fulfill the needs of society. The reasons are many, some visible and others not. SR provides the basis for being compassionate; hence, medical schools need to include SR as a criterion in their admissions process for student recruitment and inculcate compassion in health professions education and health care. By fostering SR and engaging compassion and self-compassion to achieve SA, we can humanize medical education systems and health care.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.060
GPT teacher head0.470
Teacher spread0.410 · 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 designQualitative
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

Citations2
Published2023
Admission routes1
Has abstractyes

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