Barriers to and facilitators of successful implementation of a palliative approach to care in primary care practices: a mixed methods study
Bibliographic record
Abstract
OBJECTIVE: Integrating a palliative approach to care into primary care is an emerging evidence-based practice. Despite the evidence, this type of care has not been widely adopted into primary care settings. The objective of this study was to examine the barriers to and facilitators of successful implementation of a palliative approach to care in primary care practices by applying an implementation science framework. DESIGN: This convergent mixed methods study analysed semistructured interviews and expression of interest forms to evaluate the implementation of a protocol, linked to implementation strategies, for a palliative approach to care called Early Palliation through Integrated Care (EPIC) in three primary care practices. This study assessed barriers to and facilitators of implementation of EPIC and was guided by the Consolidated Framework for Implementation Research (CFIR). A framework analysis approach was used during the study to determine the applicability of CFIR constructs and domains. SETTING: Primary care practices in Canada. Interviews were conducted between September 2020 and November 2021. PARTICIPANTS: 10 individuals were interviewed, who were involved in implementing EPIC. Three individuals from each practice were reinterviewed to clarify emerging themes. RESULTS: Overall, there were implementation barriers at multiple levels that caused some practices to struggle. However, barriers were mitigated when practices had the following facilitators: (1) a high level of intra-practice collaboration, (2) established practices with organisational structures that enhanced communications, (3) effective leveraging of EPIC project supports to transition care, (4) perceptions that EPIC was an opportunity to make a long-term change in their approach to care as opposed to a limited term project and (5) strong practice champions. CONCLUSIONS: Future implementation work should consider assessing facilitators identified in our results to better gauge primary care pre-implementation readiness. In addition, providing primary care practices with support to help offset the additional work of implementing innovations and networking opportunities where they can share strategies may improve implementation success.
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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 | 0.033 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".