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Record W7101384764 · doi:10.1093/eurpub/ckaf161.327

Organizational health literacy in primary care: a type 2 hyprid effectiveness-implementation study

2025· article· en· W7101384764 on OpenAlexaff

Bibliographic record

VenueEuropean Journal of Public Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsHealth Care Foundation
Fundersnot available
KeywordsData collectionPrimary careHealth careQualitative propertyPrimary health careNormalization (sociology)Sample (material)Literacy

Abstract

fetched live from OpenAlex

Abstract Background Promoting organizational health literacy (OHL) in primary care is crucial for public health, as it aligns healthcare services with patient needs and strengthens the health literacy of patients, caregivers, and professionals. For this purpose, the Swiss OHL Self-Assessment Tool for Primary Care (OHL Self-AsseT) was developed, accompanied by an implementation strategy. The aim of the current phase is to evaluate the effectiveness of the OHL Self-AsseT (organizational/professional health literacy) and implementation outcomes. Methods Using a type 2 hybrid effectiveness-implementation design, the study evaluates both the intervention effectiveness and implementation outcomes. For intervention evaluation, a quasi-experimental pre-post design is applied in 18-20 primary care teams. Data collection started in 2024 and will last until the end of 2025 through validated online questionnaires. The implementation outcomes are evaluated via an explanatory mixed-method design, oriented to Normalization Process Theory. Data of a subsample of 24-28 participants is collected via questionnaires and interviews. Results Results of 6 teams show an improvement in all 6 OHL dimensions and positive effects on strengthening leadership skills. Further qualitative implementation data show that participants reported an improved ability to identify areas that need change and were able to implement change while interacting with their management. Conclusions Based on the so far available data, the OHL Self-AsseT seems to offer practical and sustainable support for primary care teams in improving their OHL. Effectiveness data from the larger sample will be presented at the conference and is crucial before scaling the tool. Based on the validated implementation strategy, the tool can be seamlessly integrated into existing organizational development processes, fostering long-term improvements in health literacy on a large scale.

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)0.020
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.070
GPT teacher head0.482
Teacher spread0.413 · 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 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
Published2025
Admission routes1
Has abstractyes

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