Examining the sustainability of Screening for Distress, the sixth vital sign, in two outpatient oncology clinics: A mixed‐methods study
Bibliographic record
Abstract
BACKGROUND: Research indicates that cancer patients experience significant multifactorial distress during their journey. To address this, cancer centers are implementing Screening for Distress programs; however, little is known about the sustainability of these programs. This study sought to examine the sustainability of a Screening for Distress program in 2 cancer clinics 6 months post implementation. METHODS: A mixed-methods cross-sectional design was utilized. To determine if screening rates, screening conversations and appropriate interventions occurred and the charts of 184 consecutive patients attending the head and neck or neuro-oncology clinics over a 3 week period were reviewed. To examine the barriers and facilitators of sustainability, 16 semi-structured interviews with administrators, physicians, and nurses were conducted. RESULTS: Of the 184 charts reviewed, 163 (88.6%) had completed screening tools. Of these 163, 130 (79.8%) indicated that a conversation occurred with the patient about the identified distress as reported on the screening tool. Of the 89 (54.6%) charts where the need for an intervention was indicated, 68 (76.4%) had an intervention documented. Six oncologists, 7 nurses, and 3 administrators were interviewed, and 5 themes which influenced the sustainability of the program emerged: (1) attitudes, knowledge, and beliefs about the program; (2) implementation approach; (3) outcome expectancy of providers; (4) integration with existing practices; and (5) external factors. CONCLUSIONS: This study suggests that Screening for Distress was largely sustained, possibly due to positive attitudes and outcome expectancy. However, sustainability may be enhanced by formally integrating screening with existing practices, addressing potential knowledge gaps, and ensuring engagement with all stakeholder groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".