Understanding Personal Determinants in the Adoption of Telesurveillance in Elder Home Care by Community Health Workers
Bibliographic record
Abstract
It would be useful to better understand the personal determinants of successful interventions in the community, especially those interventions already recognized for their efficacy and efficiency, such as elder home care telesurveillance. This is a modality of health care services that transmits, via a call center on a 24/7 basis, the clinical information necessary to follow elders outside medical centers. Community health workers refer elders to this service. A qualitative research design was realized to understand why so much difference in the implementation of this service had arisen in two comparable sites previously judged receptive. The research objectives were as follows: (1) to document the personal determinants associated with telesurveillance adoption by community health workers, in two sites previously judged receptive; and (2) to point out the personal determinants that can explain successful adoption of telesurveillance. According to the Theory of Interpersonal Behavior, the results showed that habits (e.g., community health workers' knowledge of new information technologies) and perceived barriers in clinical practice were fundamental determinants in the adoption of telesurveillance.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".