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Record W4283774244 · doi:10.4103/cjrm.cjrm_39_21

Systemic challenges and resiliency in rural family practice

2022· article· en· W4283774244 on OpenAlexaffvenueabout
Sarah Lespérance, NahidRahimipour Anaraki, Shabnam Ashgari, AnnMarie Churchill

Bibliographic record

VenueCanadian Journal of Rural Medicine · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsMemorial University of NewfoundlandDalhousie University
Fundersnot available
KeywordsBurnoutEmpathyPsychologyQualitative researchPsychological resilienceGrounded theoryCoping (psychology)SociologySocial psychologyClinical psychologySocial science

Abstract

fetched live from OpenAlex

Introduction: The objective of our study was to understand how Canadian rural family physicians (RFPs) define and use resilience strategies to maintain their roles as generalists and resist burnout, while also understanding how organisational supports and systems may play a role. Methods: This was a qualitative study of RFPs with at least 1 year of experience working in rural Canada. Data were collected via semi-structured, in-depth interviews using a grounded theory approach. The participant recruitment process involved purposive and theoretical sampling, and was stopped when theoretical saturation was reached. Results: RFPs identified the following five themes related to resilience: (1) powerlessness, (2) strained work/life balance, (3) colleagues as supportive or straining, (4) living under the microscope and (5) compassion fatigue or empathy. Strategies to enhance resilience were identified at personal, community and organisational levels by participants. Conclusion: Enhancing RFPs' awareness of the specific individual and organisational strategies, as well as system-oriented solutions to maintain resilience, is of benefit to RFPs and rural and remote communities across Canada.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.067
GPT teacher head0.407
Teacher spread0.339 · 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

Citations9
Published2022
Admission routes3
Has abstractyes

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