Barriers and Facilitators to Implementing a Stepped Care Cognitive-behavioral Therapy for Insomnia in Cancer Patients: a Qualitative Study.
Bibliographic record
Abstract
Abstract Purpose: Insomnia affects 30-60% of cancer patients and tends to become chronic when left untreated. While cognitive-behavioral therapy for insomnia (CBT-I) is the recommended first-line treatment, this intervention is not readily accessible. 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 (i.e., web-based CBT-I followed, if needed, by 1-3 booster sessions) in these settings. Methods: Nine focus groups composed of a total of 43 clinicians (e.g., physicians, nurses, technologists, psychologists), six administrators, and 10 cancer patients were held. The Consolidated Framework for Implementing Research (CFIR) 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. Barriers and facilitators to the implementation of a stepped care CBT-I included individual characteristics (e.g., lack of knowledge about CBT-I); intervention characteristics (e.g., increased accessibility offered by a web-based format); inner setting characteristics (e.g., resistance to change); and process factors (e.g., motivation to offer a new service). 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. Keys to a successful implementation include accessibility, training, inclusion of stakeholders in the process, and ensuring that they are supported throughout the implementation.
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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 | 0.014 | 0.022 |
| 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 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".