Barriers and Facilitators to Implementing a Stepped Care Cognitive-behavioral Therapy for Insomnia in Cancer Patients: a Qualitative Study.
Bibliographic record
Abstract
Abstract Background: Insomnia affects between 30 to 60% of cancer patients and tends to become chronic when left untreated. Cognitive-behavioral therapy for insomnia (CBT-I) is the recommended first-line treatment for cancer-related insomnia. Yet, this treatment is not readily accessible. A stepped care intervention beginning with a self-administered (web-based) intervention appears to be a promising cost-effective approach to offer CBT-I in routine cancer care as compared to a standard face-to-face therapy. This qualitative study investigated current practices in the assessment and management of insomnia in five hospitals offering cancer care and identified the barriers and facilitators to the implementation of a stepped care CBT-I in these clinical settings. Methods: Nine focus groups composed of a total of 43 clinicians (e.g., physicians, nurses, nurse navigators, technologists, psychologists) and administrators, as well as 10 cancer patients were held. The Consolidated Framework for Implementing Research was used to develop the semi-structured interview and analyze the data. Results: Sleep difficulties are not systematically discussed in clinical practice and when a treatment is offered, most often, it is a pharmacological one. Based on the Consolidated Framework for Implementing Research, barriers to the implementation of a stepped care CBT-I included individual characteristics (e.g. lack of knowledge about CBT-I among cancer care providers, patients’ comorbidities and preferences); intervention characteristics (e.g., lack of internet access); inner setting characteristics (e.g., lack of time and resources, resistance to change); and process factors (e.g., need for prior training and engagement of all stakeholders). Facilitators were related to individual characteristics (e.g. strong beliefs in the efficacy of CBT-I); intervention characteristics (e.g. web-based format that increases accessibility at a lower cost, short- and long-term effects of CBT-I); and process factors (e.g. high motivation and commitment to offer a new service to patients). Conclusions: This qualitative study confirms the need to better address insomnia in routine cancer care and suggests that, while some barriers were mentioned, the implementation of a stepped care CBT-I is feasible provided that some conditions are met such as prior training and engagement of all stakeholders from the outset.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| 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".