Improved Delivery of Cardiovascular Care (IDOCC): Findings from Narrative Reports by Practice Facilitators
Bibliographic record
Abstract
Practice facilitation can help family physicians adopt evidence-based guidelines. However, many practices struggle to effectively implement practice changes that result in meaningful improvement. Building on our previous research, we examined the barriers to and enablers of implementation perceived by practice facilitators (PF) in helping practices to adopt the Improved Delivery of Cardiovascular Care (IDOCC) program, which took place at 84 primary care practices in Ottawa, Canada between April 2008 and March 2012. We conducted a qualitative analysis of PFs’ narrative reports using a multiple case study design. We used a combined purposeful sampling approach to identify cases that 1) reflected experiences typical of the broader sample and 2) presented sufficient breadth of experience from each project step and family practice model. Sampling continued until data saturation was reached. Team members conducted a qualitative analysis of reports using an open and axial coding style and a constant comparative approach. Barriers and enablers were divided into five constructs: structural, organizational, provider, patient, and innovation. Narratives from 13 practice sites were reviewed. A total of 8 barriers and 11 enablers were consistently identified across practices. Barriers were most commonly reported at the organizational (n = 3) and structural level, (n = 2) while enablers were most common at the innovation level (n = 6). While physicians responded positively to PFs’ presence and largely supported their recommendations for practice change, organizational and structural aspects such as lack of time, minimal staff engagement, and provider reimbursement remained too great for practices to successfully implement practice-level changes. Trial Registration: ClinicalTrials.gov, NCT00574808
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 | 0.044 | 0.273 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".