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Record W7073850880

Reassuring and managing patients with concerns about swine flu: Qualitative interviews with callers to NHS Direct

2010· article· en· W7073850880 on OpenAlexaff

Bibliographic record

VenueResearch Portal (King's College London) · 2010
Typearticle
Languageen
FieldEngineering
TopicPhotonic Crystal and Fiber Optics
Canadian institutionsResponse Biomedical (Canada)
Fundersnot available
KeywordsCredibilityQualitative researchTriageAnxietyHotlineHealth professionalsService (business)Public health
DOInot available

Abstract

fetched live from OpenAlex

Background: During the early stages of the 2009 swine flu (influenza H1N1) outbreak, the large majority of patients who contacted the health services about the illness did not have it. In the UK, the NHS Direct telephone service was used by many of these patients. We used qualitative interviews to identify the main reasons why people approached NHS Direct with concerns about swine flu and to identify aspects of their contact which were reassuring, using a framework approach. Methods: 33 patients participated in semi-structured interviews. All patients had telephoned NHS Direct between 11 and 14 May with concerns about swine flu and had been assessed as being unlikely to have the illness. Results: Reasons for seeking advice about swine flu included: the presence of unexpectedly severe flu-like symptoms; uncertainties about how one can catch swine flu; concern about giving it to others; pressure from friends or employers; and seeking 'peace of mind.' Most participants found speaking to NHS Direct reassuring or useful. Helpful aspects included: having swine flu ruled out; receiving an alternative explanation for symptoms; clarification on how swine flu is transmitted; and the perceived credibility of NHS Direct. No-one reported anything that had increased their anxiety and only one participant subsequently sought additional advice about swine flu from elsewhere. Conclusions: Future major incidents involving other forms of chemical, biological or radiological hazards may also cause large numbers of unexposed people to seek health advice. Our data suggest that providing telephone triage and information is helpful in such instances, particularly where advice can be given via a trusted, pre-existing service.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.317
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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