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We cannot staff for ‘what ifs’: the social organization of rural nurses’ safeguarding work

2011· article· en· W1562180306 on OpenAlexafffundabout
Karen MacKinnon

Bibliographic record

VenueNursing Inquiry · 2011
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsUniversity of Victoria
FundersCanadian Institutes of Health Research
KeywordsSafeguardingNursingStaffingWork (physics)Flexibility (engineering)Rural areaFocus groupHealth careMedicinePublic relationsBusinessPolitical scienceManagement

Abstract

fetched live from OpenAlex

Rural nurses play an important role in the provision of maternity care for Canadian women. This care is an important part of how rural nurses safeguard the patients who receive care in small rural hospitals. This study utilized institutional ethnography as an approach for describing rural nursing work and for exploring how nurses' work experiences are socially organized. Rural nurses advocated for safe healthcare environments by ensuring that skilled nurses were available for every shift, day and night, at their local hospital. Rural nurses noted that this work was particularly difficult for the provision of maternity care. This article explores two threads or cues to institutional organization that were identified in our interviews and observations; namely staffing and safety standards, and the need for flexibility in staffing in small rural hospitals. Rural nurses' concerns about ensuring that skilled nurses are available in small rural hospitals do not enter into current management discourses that focus on efficiency and cost savings or find a home within current discourses of patient safety 'competencies'.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.063
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0210.033
Scholarly communication0.0070.004
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.055
GPT teacher head0.335
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2011
Admission routes3
Has abstractyes

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