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Record W2157687420 · doi:10.1111/1467-9566.12368

Sensemaking and the co‐production of safety: a qualitative study of primary medical care patients

2015· article· en· W2157687420 on OpenAlexaff
Penny Rhodes, Ruth McDonald, Stephen Campbell, Gavin Daker‐White, Caroline Sanders

Bibliographic record

VenueSociology of Health & Illness · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsHealth Sciences Centre
FundersNational Institutes of Health
KeywordsNegotiationHarmQualitative researchAction (physics)Context (archaeology)Patient safetyNursingPsychologySensemakingPrimary careParticipant observationMedicineSocial psychologyPublic relationsHealth careSociologyFamily medicinePolitical science

Abstract

fetched live from OpenAlex

This study explores the ways in which patients make sense of 'safety' in the context of primary medical care. Drawing on qualitative interviews with primary care patients, we reveal patients' conceptualisation of safety as fluid, contingent, multi-dimensional, and negotiated. Participant accounts drew attention to a largely invisible and inaccessible (but taken for granted) architecture of safety, the importance of psycho-social as well as physical dimensions and the interactions between them, informal strategies for negotiating safety, and the moral dimension of safety. Participants reported being proactive in taking action to protect themselves from potential harm. The somewhat routinised and predictable nature of the primary medical care consultation, which is very different from 'one off' inpatient spells, meant that patients were not passive recipients of care. Instead they had a stock of accumulated knowledge and experience to inform their actions. In addition to highlighting the differences and similarities between hospital and primary care settings, the study suggests that a broad conceptualisation of patient safety is required, which encompasses the safety concerns of patients in primary care settings.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.033
GPT teacher head0.326
Teacher spread0.292 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations53
Published2015
Admission routes1
Has abstractyes

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