Screening new cancer patients for psychological distress using the hospital anxiety and depression scale
Bibliographic record
Abstract
The diagnosis of a life-threatening illness creates immediate psychosocial distress for the patient and his or her family. The threat is real and the rational response is to be afraid. We need to be reaching out to patients and their families and not waiting for crises. The responsibility remains with the healthcare system and psychosocial healthcare professionals to identify those who are in most need. Psychological distress is something that can be relatively easily measured and responded to when psychosocial oncology healthcare professionals are immediately available to address those needs. This paper describes the process used to gather this information, how that information has been used by the psychosocial clinicians in the Supportive Care programme, and what we have learned, in terms of a retrospective data analysis, about our patient population. At the Cancer Centre in Thunder Bay, Ontario, Canada new cancer patients complete the HADS on the day of their first appointment. Since October 2000 we have collected baseline psychological distress data for 3,035 new cancer patients who fully completed all 14 items on the HADS. Of those, 781 patients, or 25.7%, scored above cut-off points and were given a telephone call. We were able to contact 607 (or 77.7%) of these patients. Five hundred and eight (or 83.7%) of those contacted made, and subsequently attended, one or more appointments with a psychosocial counsellor.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".