Evaluating the Impact of Provincial Implementation of Screening for Distress on Quality of Life, Symptom Reports, and Psychosocial Well-Being in Patients With Cancer
Bibliographic record
Abstract
BACKGROUND: Although a number of accreditation agencies and professional societies recommend routine screening for distress (SFD) for patients with cancer, it has been integrated very slowly into clinical practice. OBJECTIVES: This evaluation investigated the impact of a large-scale SFD intervention on patients' quality of life, symptom reports, and psychosocial well-being. The SFD intervention involved (1) completion of the SFD tool by patients, (2) discussion between patient and provider about the concerns indicated, and (3) provision of appropriate assessments/interventions based on priority concerns. PATIENTS AND METHODS: This quality improvement work included a pre-evaluation and postevaluation of the impact of implementation on patients' well-being. Patients in cohort 1 (N=740) were surveyed before implementation, whereas patients in cohort 2 (N=534) were surveyed 10 months after the implementation at 17 clinics province-wide. As part of the implementation, providers received training on assessing and responding to patient priority concerns with the standardized tool. RESULTS: No differences were seen in total score of quality of life between the cohorts. Fewer patients in cohort 2 than in cohort 1 reported health problems, including tiredness, drowsiness, poor appetite, nausea, anxiety, and poor well-being. Similarly, significantly fewer patients in cohort 2 endorsed problems relating to emotional, practical, informational, spiritual, social, and physical aspects of well-being. CONCLUSIONS: Results showed significantly improved psychological and physical symptoms and psychosocial well-being after routine SFD was implemented, suggesting that a large-scale SFD intervention is beneficial for patients when it is integrated into existing clinical practice and community resources.
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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 | 0.003 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".