Physicians’ Perspectives of the Factors Influential in Implementing National Clinical Guidelines for Hypertension in Iran: A Qualitative Study
Bibliographic record
Abstract
Background: Although clinical guidelines help in the prevention, diagnosis, and management of hypertension, individuals with hypertension often remain undiagnosed, or once diagnosed, may not receive adequate care and treatment. Objectives: In this study, we explored physicians’ perspectives of the factors influential in implementing the Iranian Recommendations on Prevention, Evaluation, and Management of Hypertension (IRPEM-HTN). Methods: A qualitative descriptive study was conducted using the purposeful sampling method to enroll 30 physicians from October 2016 to May 2018. The physicians working in private offices or university-affiliated health centers/hospitals in the province of Isfahan, Iran, were included in this study one year after participating in the IRPEM-HTN program. The data were gathered through semi-structured interv iews. The transcribed data were thematically analyzed, and the rigor was ensured following the procedure outlined by Braun and Clark. Results: This themes identified were: 1) macro- and meso-contextual factors, emphasizing that a) policies at all levels affect guideline adherence, and b) environmental-contextual factors affect guideline adherence, 2) micro level factors, emphasizing that c) attributes of the guideline affect guideline adherence, and d) clinician- and patient-level factors affect guideline adherence. Micro-, meso- and macro-level factors influenced guideline implementation. Conclusion: The attitudes, awareness, knowledge, and capacity of clinicians, as well as a broader socio-cultural context should be taken into account for guideline implementation.
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 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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".