Clinician-Caregiver Engagement in Older Adult Care. Development of a Validated Caregiver Experience Survey to Inform the Optimization of the Caregiver Role
Bibliographic record
Abstract
Background: Clinicians caring for older adults often lack information on how best to engage with caregivers to optimize patient health and care experiences. The objective of this study was to build a valid survey to better understand clinician-caregiver engagement. Method: Study methods were co-designed with caregivers of older adults and geriatric medicine experts from across Ontario, Canada. Recognized survey research methods were utilized (literature review, survey framework development, draft survey items, cognitive interviews ( n = 8), pilot testing ( n = 120), and psychometric analysis). Results: The final version of the online “Caregiver Experience Survey” includes 11 core items, 1 overall item, 2 qualitative questions, and 2 demographic questions. Very high internal consistency was demonstrated among the 11 core items (Cronbach's alpha: 0.94). The correlation between the overall rating and the summed 11-core item score was 0.74, providing evidence of construct validity. Face and content validity were also demonstrated. Conclusion: This psychometrically sound online survey, which measures the degree to which caregivers experience meaningful engagement with clinicians to fulfill their caregiver role, can be used by clinicians to identify quality improvement initiatives.
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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 | 0.020 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".