Evaluation of Telenursing Outcomes: Satisfaction, Self‐Care Practices, and Cost Savings
Bibliographic record
Abstract
Info-Santé CLSC, the Québec telenursing service, is a telephone health line nursing service that was implemented in 1995 in every local community service center (CLSC; n = 141) of 15 regional health authorities in the Province of Québec, Canada. It is, at present, one of the most important first-line health services and it operates in continuity with the other resources in the health and social service system. Info-Santé CLSC operates 24 hours a day, 7 days a week, and received more than 2,260,000 calls in 1997. This article will report the findings from the first province-wide survey of the service, based on a stratified random sample of 4,696 callers. The findings revealed that most respondents were highly satisfied with the service; they followed the nurses' advice and carried out self-care measures as recommended. Nursing interventions helped respondents feel self-reliant, like they could solve the same or similar problems should they occur in the future. The vast majority of respondents considered that the call they made to Info-Santé CLSC was useful in finding a solution to their problems. The vast majority also claimed that they would certainly call Info-Sante CLSC again should another problem occur. The majority reported they would have turned to another type of resource if Info-Santé CLSC had not existed; half of the respondents stated that they would have used emergency departments and a third would have consulted a doctor in private practice.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".