Identifying barriers and facilitators towards implementing guidelines to reduce caesarean section rates in Quebec
Bibliographic record
Abstract
OBJECTIVE: To investigate obstetricians perceptions of clinical practice guidelines targeting management of labour and vaginal birth after previous caesarean birth, and to identify the barriers to, facilitators of and obstetricians solutions for implementing these guidelines in practice. METHODS: This qualitative study was conducted in three hospitals in Montreal that represent around 10% of births in Quebec. Data was collected from 10 focus groups, followed by six semi-structured interviews. Two researchers jointly analysed the verbatim transcripts according to A manual for the use of focus groups. FINDINGS: The identified barriers to and facilitators of the implementation of guidelines can be classified into four categories: 1) the hospital level, including management and hospital policies; 2) the departmental level, including local policies, leadership, organizational factors, economic incentive, and availability of equipment and staff; 3) the health professionals motivations and attitudes, including medico-legal concerns, skill levels, acceptance of guidelines and strategies used to implement recommendations; and 4) patients motivations. CONCLUSION: Identifying the barriers to and facilitators of the adoption of recommendations is an important way to guide the development of efficient strategies. The findings of this study suggest that the adoption of guidelines may be improved if local health professionals perceptions are considered to make recommendations more acceptable and useful. Our findings also support the assumption that obstetricians seek to implement best practices, but require evidence tools and support to assess their practices and enhance their performance. In addition, peer review activities championed by opinion leaders have been identified by obstetricians as the most suitable strategy to improve the use of the guidelines in their practices.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".