Do callers of young children with fever follow the self-care recommendations given by a nursing triage line?
Bibliographic record
Abstract
IntroductionThe management of fever can be a stressful situation for caregivers of young children. Accessing emergency departments and urgent care centres (ED/UCCs) due to concerns about fever and the potential consequences of child fever is common, despite fever rarely being considered a medical emergency. Objectives and ApproachDetermine the non-compliance rate with public health advice for self-care at home for young children (3-35 months) with a fever. Non-compliance was defined based on the presence of a record of healthcare use within 72 hours following a call to a nurse telephone triage line, Health Link (HL), and receiving a self-care recommendation. Callers between October 2015-March 2016 were identified and linked with four databases: registry files, National Ambulatory Care Reporting System; Inpatient-Discharge Abstract Database and Physician Claims (N = 879). Overall non-compliance rate and descriptive analysis by child age, caregiver age, geography, and call time were completed. ResultsThe overall non-compliance rate with HL advice was 35.6%. Among callers, 17.5% visited an ED/UCC, 1.1% had an inpatient hospital admission, and 21.3% visited a physician’s office. Among the patients that utilized health care services after the HL call, 13.6% only visited ED/UCC, 18% only visited a physician’s office, and 4% utilized more than one type of health care service. Callers in rural and rural remote areas had lower odds of visiting a physician’s office compared to the urban areas (p-value <0.01). No significant differences were found by child age, caregiver age or time of call. Conclusion/ImplicationsFindings of this study suggest that approximately one-third of callers are not following the telephone triage advice, potentially leading to unnecessary increased burden on the healthcare system. Further study is warranted to examine reasons for non-compliance. Strategies to increase compliance in caregivers should be explored.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".