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Record W2159317709

Building physician resilience.

2008· article· en· W2159317709 on OpenAlexaff
Phyllis Marie Jensen, Karen Trollope‐Kumar, Heather Waters, Jennifer Everson

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPrioritizationQualitative researchResilience (materials science)Psychological resilienceProcess (computing)PsychologyMedical educationFace (sociological concept)MedicineNursingApplied psychologySocial psychologyComputer scienceSociologyManagement science
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore the dimensions of family physician resilience. DESIGN: Qualitative study using in-depth interviews with family physician peers. SETTING: Hamilton, Ont. PARTICIPANTS: Purposive sample of 17 family physicians. METHOD: An iterative process of face-to-face, in-depth interviews that were audiotaped and transcribed. The research team independently reviewed each interview for emergent themes with consensus reached through discussion and comparison. Themes were grouped into conceptual categories. MAIN FINDINGS: Four main aspects of physician resilience were identified: 1) attitudes and perspectives, which include valuing the physician role, maintaining interest, developing self-awareness, and accepting personal limitations; 2) balance and prioritization, which include setting limits, taking effective approaches to continuing professional development, and honouring the self;3) practice management style, which includes sound business management, having good staff, and using effective practice arrangements; and 4) supportive relations, which include positive personal relationships, effective professional relationships, and good communication. CONCLUSION: Resilience is a dynamic, evolving process of positive attitudes and effective strategies.

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
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.092
GPT teacher head0.399
Teacher spread0.306 · 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 designObservational
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

Citations136
Published2008
Admission routes1
Has abstractyes

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