Purchasing and Using Personal Emergency Response Systems (PERS): how decisions are made by community-dwelling seniors in Canada
Bibliographic record
Abstract
BACKGROUND: As the demographic of older people continues to grow, health services that support independence among community-dwelling seniors have become increasingly important. Personal Emergency Response Systems (PERS) are medical alert systems, designed to serve as a safety net for seniors living alone. Health care professionals often recommend that seniors in danger of falls or other medical emergencies obtain a PERS. The purpose of the study was to investigate the experience of seniors living with and using a PERS in their daily lives, using a qualitative grounded theory approach. METHODS: Five focus groups and 10 semi-structured interviews, with a total of 30 participants, were completed using a grounded theory approach. All participants were PERS subscribers over the age of 80, living alone in a naturally occurring retirement community (NORC) with high health service utilization in a major urban centre in Ontario. Constant comparative analysis was used to develop themes and ultimately a model of why and how seniors obtain and use the PERS. RESULTS: Two core themes, unpredictability and decision-making around PERS activation, emerged as major features of the theoretical model. Being able to get help and the psychological value of PERS informed the context of living with a PERS. CONCLUSIONS: A number of theoretical conclusions related to unpredictability and the decision-making process around activating PERS were generated.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".