Organizational health literacy in primary care: a type 2 hyprid effectiveness-implementation study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".