Development of the Health Awareness and Behaviour Tool (HABiT): reliability and suitability for a Canadian older adult population
Bibliographic record
Abstract
BACKGROUND: Determining the effectiveness of community-based health promotion and disease prevention programs requires an appropriate data collection tool. This study aimed to develop a comprehensive health questionnaire for older adults, called the HABiT, and evaluate its reliability, content validity, and face validity in assessing individual health-related items (e.g., health status, healthcare utilization) and five specific scales: knowledge, current health behaviors (risk factors), health-related quality of life (HRQoL), perceived risk and understanding, and self-efficacy. METHODS: Iterative survey development and evaluation of its psychometric properties in a convenience sample of 28 older adults (≥ 55 years old), half from a low-income population. Following item generation, the questionnaire was assessed for content validity (expert panel), face validity (participant feedback), internal consistency of each scale (Cronbach's alpha), and test-retest reliability for each item and scale (Pearson's r and phi correlations, as appropriate). RESULTS: Questions were drawn from 15 sources, but primarily three surveys: Canadian Community Health Survey, Canadian Diabetes Risk Questionnaire (CANRISK), and a survey by the Canadian Hypertension Education Program. Expert consensus was attained for item inclusion and representation of the desired constructs. Participants completing the questionnaire deemed the questions to be clear and appropriate. Test-retest reliability for many individual items was moderate-to-high, with some exceptions for items that can reasonably change in a short period (e.g., perceived day-to-day stress). Of the five potential scales evaluated, two had acceptable internal consistency (Cronbach's alpha ≥ 0.60) and a subset of one scale also had acceptable internal consistency. Test-retest reliability was high (correlation ≥ 0.80) for all scales and sub-scales. CONCLUSIONS: The HABiT is a reliable and suitable comprehensive tool with content and face validity that can be used to evaluate health promotion and chronic disease prevention programs in older adults, including low-income older adults. Some noted limitations are discussed. Data collected using this tool also provides a diabetes risk score, health literacy score, and quality-adjusted life years (QALYs) for economic analysis.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".