VALIDITY OF A SCALE TO MEASURE COMMITMENT TO HIP PROTECTORS AMONG CARE PROVIDERS IN LONG-TERM CARE
Bibliographic record
Abstract
Hip fractures are a major threat to healthy aging, often triggering declines in health, mobility, quality of life, and even death. Wearable hip protectors represent a promising strategy to prevent hip fractures, but uncertainty surrounding their efficacy exists due to poor user adherence. In long-term care (LTC), adherence may depend on the commitment of caregivers to support use of hip protectors among residents, but empirical evidence is limited. Therefore, our objective was to develop and test the psychometric properties of a scale to measure caregiver commitment to hip protectors. We wrote 15-items to measure three components of commitment: affective (belief in value), cognitive (belief in efficacy), and behavioural (willingness to act). We surveyed 535 paid caregivers (92% female) from thirteen public LTC homes in British Columbia, Canada. Responses were subjected to exploratory factor analysis (EFA) and internal reliability testing. EFA supported a higher order structure, with two factors. Affective and cognitive items loaded highest on Factor 1, and behavioural items on Factor 2. Both factors loaded onto a higher order factor. One item was removed due to low loadings. Cronbach’s alpha coefficients for the affective/cognitive subscale, the behavioural subscale, and the full scale were .97, .87, and .96, respectively. Overall, 8% of the variability in commitment was explained by LTC home. Mean commitment was lower in caregivers with 20+ years of tenure, and those aware of a resident breaking their hip despite being protected (p<0.01). Findings could inform policy and practice to enhance caregiver commitment to hip protectors in LTC.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".