The cancer Support Person's Unmet Needs Survey
Bibliographic record
Abstract
BACKGROUND: A rigorous psychometric methodology was used to develop a measure of unmet needs for cancer survivors' principal support persons. Principal support person was defined as "someone you can count on and who helps you with your needs." METHODS: Development of the domains and the items followed an extensive literature review, iterative input from support persons, and consultation with health professionals and front-line staff working with cancer survivors and their supports. Cognitive interviews helped clarify item wording, and the draft questionnaire was reappraised by a group of support persons. The questionnaire was reduced to 90 items and sent to a stratified, random sample of cancer survivors selected from a provincial population-based cancer registry. They were asked to give the survey to their support person. RESULTS: The resulting 78-item Support Person Unmet Needs Survey has high acceptability, item test-retest reliability, internal consistency (Chronbach alpha = .990), and face, content, and construct validity. It captures 6 domains of unmet needs and accounts for 73.5% of total variance: Information and Relationship Needs (27 items, 22.1% of variance), Emotional Needs (16 items, 15.2%), Personal Needs (14 items, 14.0%), Work and Finance (8 items, 8.8%), Health Care Access and Continuity (9 items, 8.6%), and Worries About Future (4 items, 4.8%). CONCLUSIONS: This instrument will be of use where there is an interest in examining the impact of cancer not only on cancer survivors but also on their identified principal support persons.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".