Effects of a Provincial-Wide Implementation of Screening for Distress on Healthcare Professionals' Confidence and Understanding of Person-Centered Care in Oncology
Bibliographic record
Abstract
BACKGROUND: Although published studies report that screening for distress (SFD) improves the quality of care for patients with cancer, little is known about how SFD impacts healthcare professionals (HCPs). OBJECTIVES: This quality improvement project examined the impact of implementing the SFD intervention on HCPs' confidence in addressing patient distress and awareness of person-centered care. PATIENTS AND METHODS: This project involved pre-evaluation and post-evaluation of the impact of implementing SFD. A total of 254 HCPs (cohort 1) were recruited from 17 facilities across the province to complete questionnaires. SFD was then implemented at all cancer care facilities over a 10-month implementation period, after which 157 HCPs (cohort 2) completed post-implementation questionnaires. At regional and community care centers, navigators supported the integration of SFD into routine practice; therefore, the impact of navigators was examined. RESULTS: HCPs in cohort 2 reported significantly greater confidence in managing patients' distress and greater awareness about person-centered care relative to HCPs in cohort 1. HCPs at regional and community sites reported greater awareness in person-centeredness before and after the intervention, and reported fewer negative impacts of SFD relative to HCPs at tertiary sites. Caring for single or multiple tumor types was an effect modifier, with effects observed only in the HCPs treating multiple tumors. CONCLUSIONS: Implementation of SFD was beneficial for HCPs' confidence and awareness of person-centeredness. Factors comprising different models of care, such as having site-based navigators and caring for single or multiple tumors, influenced outcomes.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".