CanDirect: Effectiveness of a Telephone-Supported Depression Self-Care Intervention for Cancer Survivors
Bibliographic record
Abstract
PURPOSE Depression in post-treatment cancer survivors is common and can impair quality of life. CanDirect is a novel, telephone-delivered depression self-care intervention for cancer survivors. We conducted a randomized controlled superiority trial to compare CanDirect with usual care (UC) in this population. METHODS Participants completing cancer treatment within the past 10 years who had mild-moderate depressive symptoms with or without major depression were recruited from clinical and community settings in Quebec and Ontario. Permuted block random assignment allocated participants to CanDirect plus UC or to UC alone. Assessments of depression severity (Center for Epidemiological Studies-Depression scale [CES-D]; primary outcome) and secondary outcomes health-related quality of life (Short Form Survey-12 mental and physical component summaries), anxiety symptoms (Hospital Anxiety and Depression Scale), activation (Patient Activation Measure), depression diagnosis (Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders-IV), and health services (self-report) were conducted at baseline, as well as 3 and 6 months (primary time point). Analyses of outcomes were adjusted for covariates using linear regression and missing data by inverse probability weighting. RESULTS Participants recruited between September 2016 and October 2018 were randomly assigned to CanDirect (n = 121) or UC (n = 124). Among 245 participants randomly assigned, 218 (89.0%) completed the primary outcome at 6 months. CanDirect participants reported less severe depressive symptoms on the CES-D than UC participants at 6 months, adjusted effect size (ES) 0.61 (95% CI, 0.33 to 0.88). CanDirect participants also had significantly greater quality of life, lower anxiety, more activation, and lower rates of depression diagnoses, compared with UC. Exploratory analysis suggested that sex was a modifier of the primary outcome (interaction term P value = .03); the intervention was less effective in men (ES, 0.12; 95% CI, −0.45 to 0.69). CONCLUSION The findings suggest that CanDirect is an effective method of managing mild-moderate depression symptoms in cancer survivors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".