Barriers and facilitators of implementing a multicomponent intervention to improve faecal immunochemical test (FIT) colorectal cancer screening in primary care clinics, Alberta
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Colorectal cancer (CRC) screening is effective at reducing the incidence and mortality of CRC. To address suboptimal CRC screening rates, a faecal immunochemical test (FIT) multicomponent intervention was piloted in four urban multidisciplinary primary care clinics in Alberta from September 2021 to April 2022. The interventions included in-clinic distribution of FIT kits, along with FIT-related patient education and follow-up. This study explored barriers and facilitators to implementing the intervention in four primary clinics using the Consolidated Framework for Implementation Research (CFIR). METHODS: In-depth qualitative semistructured key informant interviews, guided by the CFIR, were conducted with 14 participants to understand barriers and facilitators of the FIT intervention implementation. Key informants were physicians, quality improvement facilitators and clinical staff. Interviews were analysed following an inductive-deductive approach. Implementation barriers and facilitators were organised and interpreted using the CFIR to facilitate the identification of strategies to mitigate barriers and leverage facilitators for implementation at the clinic level. RESULTS: Key implementation facilitators reported by participants were patient perceived needs being met; the clinics' readiness to implement FIT, including staff's motivation, skills, knowledge, and resources to implement; intervention characteristics-evidence-based, adaptable and compatible with existing workflows; regular staff communications; and use of the electronic medical record (EMR) system. Key barriers to implementation were patient's limited awareness of FIT screening for CRC and discomfort with stool sample collection; the impacts of COVID-19 (patients missed appointment, staff coordination and communication were limited due to remote work); and limited clinic capacity (knowledge and skills using EMR system, staff turnover and shortage). CONCLUSION: Findings from the study facilitate the refinement and adaption of future FIT intervention implementation. Future research will explore implementation barriers and facilitators in rural settings and from patients' perspectives to enhance the spread and scale of the intervention.
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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 | 0.005 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".