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Record W4389223133 · doi:10.1080/13561820.2023.2280586

Optimizing rural healthcare through improved team function: a case study of the Rural Surgical Obstetrical Networks programme

2023· article· en· W4389223133 on OpenAlexaffabout
Jude Kornelsen, Hilary Ho, Kim M. Williams, Tom Skinner

Bibliographic record

VenueJournal of Interprofessional Care · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsCapital Regional DistrictUniversity of British Columbia
Fundersnot available
KeywordsHealth careFunction (biology)MedicineNursingMedical educationPolitical science

Abstract

fetched live from OpenAlex

We explored enablers and mechanisms of optimal team function within rural hospital teams, and the impact of these factors on health service sustainability in British Columbia. The data were drawn from interviews and focus groups with healthcare providers and administrators (n = 169) who participated in the Rural Surgical Obstetrical Networks (RSON) initiative to support low-volume rural surgical and obstetrical services in British Columbia, Canada. The 5-year programme (2018–2022) provided evidence-based system interventions across eight rural sites with the objective of providing sustainable, quality health services to meet population needs. To explore the impact of RSON interventions on local team function, we performed a scoping review, to assess the current literature surrounding enablers of effective rural hospital teamwork. Through inductive thematic analysis of interview data, we identified five enablers of good team function at RSON sites, including emphasis on local leadership, shared direction, commitment to sustainability, respect and solidarity among colleagues, and meaningful communication. The RSON project led to a shift in team culture in participating sites, improved team function, and contributed to improved clinical processes and patient outcomes. The findings have implications for rural health policy and practice in British Columbia and other jurisdictions with similar health service delivery models and geographic contexts.

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 categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.004
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 head0.053
GPT teacher head0.431
Teacher spread0.378 · 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 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

Citations5
Published2023
Admission routes2
Has abstractyes

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