Explaining Factors Influencing the Establishment of Health-Promoting Hospitals: A National Study using Path Analysis
Bibliographic record
Abstract
Objective(s): Following the first statement of health promotion in Ottawa World Summit, a new look on the provision of services by hospitals was born, and Health Promoting Hospitals (HPH) by the World Health Organization was introduced.Hence, this paper aimed to use path analysis to determine the factors influencing the establishment of Health Promoting Hospitals (HPH).Methods: This was a cross-sectional study that was conducted in 2021 using cluster sampling at medical sciences universities in four provinces (Tehran, Gilan, Isfahan, Fars).The study sample included 230 university professors, managers and nursing experts with academic, executive and managerial experiences in health promotion.The research instrument was a self-designed questionnaire.Data analysis was performed using path analysis.Result: The most direct impact was related to the community variable and all direct and indirect relationships (except for the direct impact of planning and evaluation variables) were significant and effective in the establishment of health-promoting hospitals.Conclusions: Considering the direct impact of the community components in establishing a health promoting hospital, it is essential for the staff and team implementing health programs in the hospital to review, identify, and prioritize community health issues and problems.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".