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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".