Cancer distress screening data: Translating knowledge into clinical action for a quality response
Bibliographic record
Abstract
OBJECTIVES: The purpose of this paper is to summarize the use of the knowledge to action framework for adapting guidelines for practice and the evidence for effective implementation interventions to promote a quality response to cancer distress screening data. METHODS: We summarize progress in screening implementation in Ontario, Canada and the application of a systematic approach for adapting knowledge to practice and use of evidence-based knowledge translation interventions to ensure the uptake of best practices to manage distress. RESULTS: While significant progress has been made in the uptake of distress screening it is less clear if this has resulted in improvements in patient outcomes, i.e., reduced distress. The use of evidence-based knowledge translation strategies tailored to barriers at many levels of care delivery is critical to facilitate the uptake of distress screening data by the primary oncology team. SIGNIFICANCE OF RESULTS: There is a wealth of knowledge about the approaches that can be applied to translate knowledge into practice to improve psychosocial care and promote evidence-based distress management by the primary care oncology team. However, further implementation research is needed to advance knowledge about the most effective strategies in the context of cancer care.
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 | 0.041 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".