Screening new cancer patients for psychological distress using the hospital anxiety and depression scale
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it