A national study of the unmet needs of support persons of haematological cancer survivors in rural and urban areas of Australia
Bibliographic record
Abstract
PURPOSE: This study aimed to compare support persons of haematological cancer survivors living in rural and urban areas in regard to the type, prevalence and factors associated with reporting unmet needs. METHODS: One thousand and four (792 urban and 193 rural) support persons of adults diagnosed with haematological cancer were recruited from five Australian state population-based cancer registries. Participants completed the Support Person Unmet Needs Survey (SPUNS) that assessed the level of unmet needs experienced over the past month across six domains. RESULTS: Overall, 66% of support persons had at least one 'moderate, high or very high' unmet need and 24% (n = 182) reported having multiple (i.e. 6 or more) 'high/very high' unmet needs in the past month. There were no significant differences between rural and urban support persons in the prevalence of multiple unmet needs or mean total unmet needs scores. There were however significant differences in the types of 'high/very high' unmet needs with support persons living in rural areas more likely to report finance-related unmet needs. Support persons who indicated they had difficulty paying bills had significantly higher odds of reporting multiple 'high/very high' unmet needs. CONCLUSIONS: This is the first large, population-based study to compare the unmet needs of support persons of haematological cancer survivors living in rural and urban areas. Findings confirm previous evidence that supporting a person diagnosed with haematological cancer correlates with a high level of unmet needs and highlight the importance of developing systemic strategies for assisting support persons, especially in regard to making financial assistance and travel subsidies known and readily accessible to those living in rural areas.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".