Investigating the Pre‐Implementation Facilitators and Barriers of the Implementation of the Fear of Recurrence Therapy (FORT) Intervention in Canadian Cancer Centers
Bibliographic record
Abstract
OBJECTIVES: Fear of cancer recurrence (FCR) is the number one unmet psychosocial need of cancer survivors. Fortunately, several interventions have demonstrated their efficacy in reducing FCR in randomized controlled trials (RCTs), including the Fear of Recurrence Therapy (FORT) intervention, a 6-week, cognitive-existential group therapy. However, few interventions are implemented in routine clinical care. The present study aims to document pre-implementation facilitators and barriers from the perspectives of clinicians and decision-makers to prepare the implementation of FORT in Canadian cancer centers. METHODS: This mixed-methods comparative case study evaluated the process of implementing FORT in 5 Canadian clinical sites. Prior to implementation, we conducted individual semi-structured interviews with clinicians and decision-makers at each site, based on the Consolidated Framework for Implementation Research (CFIR), to uncover barriers and facilitators of implementation. Content analysis was performed on the interviews using the NVivo template provided by the CFIR. RESULTS: We interviewed 20 managers/decision-makers and clinicians who reported facilitators common to all sites: (1) an awareness of the need for an FCR intervention; (2) the perceived benefit of FORT's group format to reduce waitlists for individual FCR services; and (3) that offering an evidence-based intervention was within the mission of their institution. All sites identified staff shortage and concerns for equitable access to FORT as the main barriers. Each site had additional unique barriers. CONCLUSION: This analysis of facilitators and barriers will directly contribute to the selection of site-specific strategies and tools to optimize the implementation of FORT.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".