Nurses’ experiences with telephone triage and advice: a meta‐ethnography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it