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Record W2750347115 · doi:10.1093/fampra/cmx078

General practitioners’ perspective on poverty: a qualitative study in Montreal, Canada

2017· article· en· W2750347115 on OpenAlexafffundabout
Christine Loignon, Thomas Gottin, Sophie Dupéré, Christophe Bedos

Bibliographic record

VenueFamily Practice · 2017
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsMcGill UniversityUniversité LavalUniversité de Sherbrooke
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsMedicinePerspective (graphical)PovertyQualitative researchFamily medicineGerontologyEconomic growthSocial science

Abstract

fetched live from OpenAlex

Background: Social inequalities in healthcare systems persist worldwide. Physicians' prejudices and negative attitudes towards people living in poverty are one of the determinants of healthcare inequalities. We know very little about general practitioners' (GPs) perceptions of poverty, which shape their attitudes. Objective: To identify the perceptions of poverty of GPs who deal with it in everyday practice. Methods: A qualitative study based on interviews with GPs working in deprived urban neighbourhoods. In-depth semi-structured interviews were conducted with physicians working in disadvantaged neighbourhoods in Montreal, Canada. Interviews were audio-recorded and transcribed verbatim. Analysis consisted of interview debriefing, transcript coding, and thematic analysis using an inductive and iterative approach. Results: Our study revealed two contrasting perceptions of poverty. The global conception of poverty referred to social determinants and was shared by the majority of physicians interviewed, while the moral conception, centring on individual responsibility, was shared by a minority of participants. Conclusion: The moral judgments and misunderstandings evidenced by GPs regarding poverty suggest avenues for improving general medical training. Understanding social determinants of health should be an important component of this training, to improve access to care for people living in poverty.

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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
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 head0.245
GPT teacher head0.545
Teacher spread0.300 · 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

Citations10
Published2017
Admission routes3
Has abstractyes

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