Brief Communication: Screening for distress in community settings
Bibliographic record
Abstract
Purpose: This project was designed to develop, refine and field-test a distress screening approach with survivors accessing community-based cancer support agencies. Methods: The project was conducted in phases including a literature review and focus groups with cancer survivors and community agency staff. Data were gathered to lay the foundation for building a subsequent development and implementation of a new screening approach suitable for community-based cancer support agencies to use in identifying psychosocial distress in their clients. Results: Standardized questionnaires used for distress screening approaches in clinical settings were not seen by cancer survivors as appropriate for community-based cancer support settings. A new screening approach was designed and implemented based on input from cancer survivors and staff in community-based agencies. The tool used in the distress screening approach focused on problems relevant to individuals in the community setting. If problems were identified, staff followed tailored care pathways to resolve them. Both patients and staff found the screening approach useful for quickly pinpointing problems and avenues for dealing with the issues. Conclusions: Screening for distress approaches can be useful in a community-based cancer support setting to identify individuals who are at greater risk for experiencing difficulties. Data from screening can be useful for agencies to report on their service effectiveness.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".