Experiences of practice facilitators working on the Improved Delivery of Cardiovascular Care project: Retrospective case study.
Bibliographic record
Abstract
OBJECTIVE: To examine the barriers to and facilitators of practice facilitation experienced by participants in the Improving Delivery of Cardiovascular Care (IDOCC) project. DESIGN: Case studies of practice facilitators' narrative reports. SETTING: Eastern Ontario. PARTICIPANTS: Primary care practices that participated in the IDOCC project. MAIN OUTCOME MEASURES: Cases were identified by calculating sum scores in order to determine practices' performance relative to their peers. Two case exemplars were selected that scored within ± 1 SD of the total mean score, and a qualitative analysis of practice facilitators' narrative reports was conducted using a 5-factor implementation framework to identify barriers and facilitators. Narratives were divided into 3 phases: planning, implementation, and sustainability. RESULTS: Barriers and facilitators fluctuated over the intervention's 3 phases. Site A reported more barriers (n = 47) than facilitators (n = 38), while site B reported a roughly equal number of barriers (n = 144) and facilitators (n = 136). In both sites, the most common barriers involved organizational and provider factors and the most common facilitators were associated with innovation and structural factors. CONCLUSION: Both practices encountered various barriers and facilitators throughout the IDOCC's 3 phases. The case studies reveal the complex interactions of these factors over time, and provide insight into the implementation of practice facilitation programs.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".