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Record W2885666535 · doi:10.1093/heapro/day047

Adapting a health equity tool to meet professional needs (Québec, Canada)

2018· article· en· W2885666535 on OpenAlexaffabout
Anne Guichard, Émilie Tardieu, Kareen Nour, Ginette Lafontaine, Valéry Ridde

Bibliographic record

VenueHealth Promotion International · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversité de MontréalSanté MontérégieUniversité Laval
Fundersnot available
KeywordsEquity (law)Health equityPublic relationsKnowledge managementBusinessAdaptation (eye)PopulationHealth literacyPsychologyHealth carePolitical scienceMedicineNursingPublic healthComputer science

Abstract

fetched live from OpenAlex

While numerous tools are available to better incorporate equity into population health actions, they are limited mainly by their lack of adaptation to professional practices and organizational realities. A study was conducted in Québec to identify and understand, from the perspective of future users, conditions that would facilitate use of a tool (Reflex-ISS) targeted at supporting collaborative action to improve consideration of social inequalities in health (SIH) within population health actions. Concept mapping and focus groups were implemented as complementary methods for investigating the conditions. Significant results that emerged were strong participant interest in the tool and the need for resources to better take SIH into account. The conditions for use that were identified referred to the tool itself (user-friendliness and literacy) and to resources for appropriating the tool, competency development, as well as the role and responsibilities of organizations and policies in promoting use of the tool in daily activities and more fundamentally in acting against SIH in general. Models for organizational innovation give an idea of the dimensions that need to be considered to strengthen the integration of equity into organizations and to support the changes in practice that result from using the tool. They provide a reminder that a health equity tool cannot be the cornerstone of an organizational strategy to fight against SIH; rather, it must be incorporated as part of a systemic strategy of professional and organizational development.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.598
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.518
GPT teacher head0.661
Teacher spread0.143 · 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
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

Citations26
Published2018
Admission routes2
Has abstractyes

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