Health care provider communication
Bibliographic record
Abstract
BACKGROUND: Patients who are facing life-threatening and life-limiting cancer almost invariably experience psychological distress. Responding effectively requires therapeutic sensitivity and skill. In this study, we examined therapeutic effectiveness within the setting of cancer-related distress with the objective of understanding its constituent parts. METHODS: Seventy-eight experienced psychosocial oncology clinicians from 24 health care centers across Canada were invited to participate in 3 focus groups each. In total, 29 focus groups were held over 2 years, during which clinicians articulated the therapeutic factors deemed most helpful in mitigating patient psychosocial distress. The content of each focus group was summarized into major themes and was reviewed with participants to confirm their accuracy. Upon completion of the focus groups, workshops were held in various centers, eliciting participant feedback on an empirical model of therapeutic effectiveness based on the qualitative analysis of focus group data. RESULTS: Three primary, interrelated therapeutic domains emerged from the data, forming a model of optimal therapeutic effectiveness: 1) personal growth and self-care (domain A), 2) therapeutic approaches (domain B), and 3) creation of a safe space (domain C). Areas of domain overlap were identified and labeled accordingly: domain AB, therapeutic humility; domain BC, therapeutic pacing; and domain AC, therapeutic presence. CONCLUSIONS: This empirical model provides detailed insights regarding the elements and pedagogy of effective communication and psychosocial care for patients who are experiencing cancer-related distress.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.160 | 0.018 |
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".