Psychological morbidity among Australian rural and urban support persons of haematological cancer survivors: Results of a national study
Bibliographic record
Abstract
OBJECTIVE: To compare the prevalence of anxiety, depression, and stress among rural and urban support persons of haematological cancer survivors and explore factors associated with having one or more of these outcomes. METHODS: Haematological cancer survivors were identified via 1 of 5 state-based cancer registries and invited to take part in a survey. Those who agreed were asked to pass on a questionnaire package to their support person. Measures included the Depression, Anxiety, and Stress Scale, Support Persons' Unmet Need Survey, and sociodemographic questions. RESULTS: Nine-hundred and eighty-nine (66%) participating survivors had a participating support person. There were no significant differences in the proportion of urban versus rural support persons who reported elevated levels of depression (21% vs 23%), anxiety (16% vs 17%), or stress (16% vs 20%), P > .05. Odds of reporting at least 1 indicator of psychological morbidity increased by 10% to 17% for each additional high or very high unmet need and by 2% for those who had relocated from their usual place of residence for the survivor to receive treatment and was decreased by 5% to 54% for those support persons who reported that they had no chronic health conditions. CONCLUSIONS: Psychological outcomes for rural and urban support persons are similar. Those who have poor health, have had to relocate, and who have multiple unmet needs are particularly vulnerable to poor psychological outcomes. These factors should be assessed to enable early intervention for those at risk of poor outcomes.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".