Impact of Telephone Triage on Medical Service Use: Implications for Rural and Remote Areas
Bibliographic record
Abstract
Lack of ready access to health services is a continuing problem for the one-quarter of northern Ontario residents who live in non-urban areas. Teletriage has been proposed to help reduce medically unnecessary visits and thus free-up available resources. A mail survey obtained information about patients' use of teletriage and other health care services, as well as socioeconomic and demographic characteristics. Survey data (n = 2389) were used to determine the impact of teletriage on health service use by asking about the patient's intended use of health care service (intent), the service advised by the nurse (advice), and the actual health service used by the patient (action). Responses were compared among geographic categories based on commuter flows to urban areas in northern Ontario. Survey responses suggest that teletriage may have decreased visits to emergency departments relative to patient intent, and this effect appears to be stronger in communities with weak or no commuter flows (intent = 54%, action = 41%) than in urban areas (intent = 39%, action = 33%). Visits to physicians' offices or clinics may have increased relative to patient intent, but only for non-urban areas (intent = 16%, action = 21% to 23%) with strong, moderate, weak, or no commuter flows. Very little difference was found among geographic categories for calls or visits to other health care providers (overall: intent = 17%, action = 11%) or for informal care (self-care and care for others) (overall: intent = 16%, action = 29%). Results should be interpreted carefully, as there was evidence of selection and social desirability bias.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".