Implementing screening for distress, the 6th vital sign: a Canadian strategy for changing practice
Bibliographic record
Abstract
OBJECTIVE: Distress is prevalent among cancer patients at all stages of illness and has been endorsed as the 6th Vital Sign in cancer care. Despite its prevalence, and calls to be monitored, few cancer programs are Screening for Distress in a standardized manner. In this paper, the implementation strategy employed in Canada to change practice by integrating Screening for Distress in routine care is described. METHODS: The process from inception of the concept of distress to the implementation of Screening for Distress is discussed. Pioneering work pertinent in laying the foundation for Screening for Distress as a National initiative is highlighted. Additionally, the experience of four jurisdictions currently Screening for Distress is utilized to demonstrate steps to successful implementation and strategies for overcoming challenges. RESULTS: Integrating Screening for Distress into practice requires endorsements from key stakeholders, developing and disseminating national recommendations and guidelines, and utilizing a coordinated and standardized method focused on practice change. At a local level successful implementations engage stakeholders, provide thorough and targeted education, establish interprofesionnal teams, and utilize a phased approach to implementation. Common challenges cited include time, buy-in and lack of resources. CONCLUSIONS: Establishing a national approach to implementing Screening for Distress is both feasible and beneficial. A coordinated approach encourages collaboration beyond the walls of any particular center and provides the opportunity for all patients to be provided with improved person-centered 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 | 0.033 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".