Caller self‐care decisions following teletriage advice
Bibliographic record
Abstract
AIM: To examine caller self-care decisions following teletriage advice provided by nurses. BACKGROUND: The use of teletriage is gaining popularity as one way of enhancing capacity for self-care. Research from several countries suggests that teletriage reduces the use of other healthcare services without compromising safety. However, there is little or no research related to how often self-care advice is provided and whether or not callers follow the advice. DESIGN: A descriptive survey design was used with a random sample of 312 callers who were advised by a teletriage nurse to engage in self-care. METHOD: Callers were randomly selected from all calls to a teletriage service each day of the month for nine months. Data were collected using a researcher-developed interview guide and analysed using a variety of inferential statistics for forced choice questions and content analysis for open-ended questions. RESULTS: The majority of callers who were advised to engage in self-care reported doing so. Callers with greater self-efficacy and satisfaction with the nurse interaction were more likely to follow advice to self-care. All callers would call the teletriage service again for the same or a different issue. CONCLUSION: Teletriage callers were confident in the advice provided and were willing to continue to use the service. RELEVANCE TO CLINICAL PRACTICE: This study indicates that teletriage programmes are a cost-effective way of addressing self-care needs of individuals who might otherwise visit an emergency department.
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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 | 0.025 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".