A pre-implementation examination of barriers and facilitators of an electronic prospective surveillance model for cancer rehabilitation: a qualitative study
Bibliographic record
Abstract
BACKGROUND: An electronic Prospective Surveillance Model (ePSM) uses patient-reported outcomes to monitor symptoms along the cancer pathway for timely identification and treatment. Randomized controlled trials show that ePSMs can effectively manage treatment-related adverse effects. However, an understanding of optimal approaches for implementing these systems into routine cancer care is limited. This study aimed to identify barriers and facilitators prior to the implementation of an ePSM to inform the selection of implementation strategies. METHODS: A qualitative study using virtual focus groups and individual interviews was conducted with cancer survivors, oncology healthcare providers, and clinic leadership across four cancer centres in Canada. The Consolidated Framework for Implementation Research (CFIR) guided the interviews and analysis of barriers and facilitators based on five domains (intervention characteristics, individual characteristics, inner setting, outer setting, and process). RESULTS: We conducted 13 focus groups and nine individual interviews with 13 patient participants and 56 clinic staff. Of the 39 CFIR constructs, 18 were identified as relevant determinants to the implementation. The adaptability, relative advantage, and complexity of an ePSM emerged as key intervention-level factors that could influence implementation. Knowledge of the system was important at the individual level. Within the inner setting, major determinants were the potential fit of an ePSM with clinical workflows (compatibility) and the resources that could be dedicated to the implementation effort (readiness for implementation). In the outer setting, meeting the needs of patients and the availability of rehabilitation supports were key determinants. Engaging various stakeholders was critical at the process level. CONCLUSIONS: Improving the implementation of ePSMs in routine cancer care has the potential to facilitate early identification and management of treatment-related adverse effects, thereby improving quality of life. This study provides insight into important factors that may influence the implementation of an ePSM, which can be used to select appropriate implementation strategies to address these factors.
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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 | 0.034 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".