Follow-through after calling a nurse telephone advice line: a population-based study
Bibliographic record
Abstract
BACKGROUND: Nurse telephone advice (NTA) lines, a major initiative in primary health care reform, provide symptom triage and health information. Compliance studies utilizing database analysis are frequently limited to a defined population, such as children or Emergency Department (ED) users. OBJECTIVES: To explore caller characteristics associated with following NTA advice to go to the ED, see a health care professional or self-care for Calgary, Canada (population 1 million). METHODS: NTA data were linked with utilization data to assess ED and physician visits following a call. Four nurse advice categories were defined: go to ED, health care provider in 24 hours, health care provider in 72 hours if symptoms persist and self-care. Follow-through was defined based on health care utilization within specified time periods following the call. Logistic regression identified characteristics associated with follow-through of NTA nurse advice; characteristics included age, sex, neighbourhood income, health status, time of call and type of care protocol. RESULTS: Follow-through was highest for self-care advice (83.7%), followed by ED advice (52.3%) and then 24-hour advice (43.2%). Lower follow-through on ED or 24-hour advice was associated with age <4 years, and having lower income, and the opposite was true for self-care advice. Patients with a cardiac complaint had the highest odds of following ED advice. Patients with a gastrointestinal or obstetrics/gynaecology/genitourinary complaint were less likely to follow 24-hour advice. Patients with fever were less likely to follow self-care advice. CONCLUSIONS: Understanding characteristics associated with lower follow-through may help the NTA service to refine its approaches to clients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".