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Record W3197149319 · doi:10.1111/ajr.12762

Retention of General Practitioners in remote areas of Canada and Australia: A meta‐aggregation of qualitative research

2021· review· en· W3197149319 on OpenAlex
Lara Wieland, Jennifer Ayton, Gail Abernethy

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAustralian Journal of Rural Health · 2021
Typereview
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsQualitative researchPerceptionNursingWork (physics)BurnoutMedicineGeneral practicePublic relationsPsychologySociologyPolitical scienceFamily medicineEngineering

Abstract

fetched live from OpenAlex

OBJECTIVE: Our aim was to systematically review qualitative evidence regarding the experiences and perceptions of General Practitioners and the factors influencing retention in remote areas of Canada and Australia. The objectives were to identify gaps and inform policy to improve retention of remote doctors, which should in turn reduce health inequalities for remote communities. DESIGN: Meta-aggregation of qualitative studies of General Practitioners and general practice registrars who had worked in a remote area of Australia or Canada for a minimum of 1 year and/or were intending to stay remote long term in their current placement. RESULTS: Six synthesised findings were identified: peer and professional support, organisational support, uniqueness of remote lifestyle and work, burnout and time off, personal family issues and cultural and gender issues. CONCLUSIONS: Long-term retention of doctors in remote areas of Australia and Canada is influenced by a range of negative and positive perceptions, and experiences with key factors being professional, organisational and personal. All 6 synthesised findings span a spectrum of policy domains and service responsibilities, and therefore, a central coordinating body could be well placed to implement a multifactorial retention strategy.

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.

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)0.013
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.338
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
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 head0.509
GPT teacher head0.627
Teacher spread0.118 · 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