Nurses’ experiences with telephone triage and advice: a meta‐ethnography
Bibliographic record
Abstract
AIMS: This study is a meta-ethnography of nurses' experiences with telephone triage and advice and factors that facilitate or impede their decision-making process. BACKGROUND: Telephone triage and advice services are a rapidly expanding development in health care. Unlike traditional forms of nursing practice, telenurses offer triage recommendations and advice to the general public without visual cues. DATA SOURCES: Published qualitative research on telephone triage and advice were sought from interdisciplinary research databases (1980-2008) and bibliographical reviews of retrieved studies. REVIEW METHODS: Our systematic search identified 16 relevant studies. Two researchers independently reviewed, critically appraised, and extracted key themes and concepts from each study. We followed techniques of meta-ethnography to synthesize the findings, using both reciprocal and refutational translation to compare similar or contradictory findings, and a line-of-arguments synthesis. RESULTS: We identified five major themes that highlight common issues and concerns experienced by telenurses: gaining and maintaining skills, autonomy, new work environment, holistic assessment, and stress and pressure. A line-of-arguments synthesis produced a three-stage model that describes the decision-making process used by telenurses and highlights how assessments largely depend on the ability to 'build a picture' of the patient and the presenting health issue. CONCLUSION: Telenurses experience a range of common concerns and issues which either impede or facilitate the decision-making process. Although 'building a picture' of the patient is key to making assessments over the telephone, final triage decisions are influenced by balancing the conflicting demands of being both carer and gatekeeper to limited healthcare services.
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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 | 0.045 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".