Evidence-Based Practice in Psychosocial Oncology from the Perspective of Canadian Service Directors
Bibliographic record
Abstract
Evidence-based practices facilitate the effective delivery of psychological services, yet research on the implementation of evidence-based practices in psychosocial oncology (PSO) is scarce. Responding to this gap, we interviewed a diverse sample of 16 directors of Canadian psychosocial oncology services about (a) how evidence-based practices in psychosocial oncology are being implemented in clinical care and how the service quality is monitored and (b) what are barriers and facilitators to evidence-based practice in psychosocial oncology services? Responses were grouped according to three main themes emerging from the data: screening for distress and referral to PSO services, delivery of evidence-based PSO services, and monitoring of PSO services. Our findings highlight facilitators and barriers to evidence-based practice in psychosocial oncology, which were related to the political, social, economic, and geographic contexts. The stepped care model was identified as a science-informed approach to improve the cost-effectiveness of triage systems and treatment delivery while facilitating more equitable access to services. Other facilitators included electronic screening and referral systems as well as protected time for clinicians to communicate more within their teams and participate in knowledge exchange. High caseloads presented a major barrier to acquiring and implementing evidence-based practices. Recommen-dations include increased support for evidence-based onboarding and continued training as well as for data collection regarding service needs, quality, and quantity to inform service monitoring and advocacy for more financial resources. Our findings are relevant to healthcare decision makers, implementation researchers, as well as service directors and practitioners providing psychosocial oncology care.
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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 | 0.053 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".