Prevalence and risk factors for suicidality in cancer patients and oncology healthcare professionals strategies in identifying suicide risk in cancer patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim of this study was to summarize the literature on prevalence and risk factors for suicidality in cancer patients and to document the research on oncology healthcare professionals' strategies in identifying this risk. RECENT FINDINGS: Cancer patients exhibit increased risk of suicidality compared with the general population. Various risk factors have been identified including sociodemographic factors such as poverty, being male and elderly as well as disease-related attributes such as cancer type and stage. The literature on how healthcare professionals identify suicide risk is sparse. Ten articles were found that focused on two main themes. These included information on systematic strategies in identifying suicide risk and factors that affect healthcare professionals' ability to identify risk in their patients. SUMMARY: Although there is an immense amount of literature documenting the problem of suicidality among patients, the research on how healthcare professionals identify and respond to these indications in patients is nearly nonexistent. Cancer centres should implement standardized and systematic screening of cancer patients for suicidality and research on this patient population should collect and report these data. Ongoing training and education for healthcare professionals who work in the oncology setting on how to identify and respond to suicide risk among cancer patients is urgently needed.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".