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Record W2995042018 · doi:10.1177/2158244019894280

When Social Inequalities Produce “Difficult Patients”: A Qualitative Exploration of Physicians’ Views

2019· article· en· W2995042018 on OpenAlexaffabout
Estelle Carde

Bibliographic record

VenueSAGE Open · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsFeelingDisadvantagedQualitative researchPerceptionInequalitySocial psychologyPsychologyHealth careSocial inequalitySet (abstract data type)Exploratory researchAction (physics)MedicineSociology

Abstract

fetched live from OpenAlex

“Difficult patients” are most often characterized by their personality, behavior, or pathology. Little research has been done to understand how some patients, because of their disadvantaged social position, are perceived as “difficult” by their physicians. Our qualitative, exploratory study was conducted to understand how social inequalities contribute to this perception of a “difficult patient.” It was based on 12 semi-structured interviews with physicians, in Montréal, Canada. Participants identified three main challenging factors: a perception of excessive time required to manage these patients; a feeling of ineffectiveness, despite this additional time spent; and the pressure to “do something” about needs they perceive as serious, despite this feeling of ineffectiveness. To adjust their practice to the specific circumstances of these disadvantaged patients, they feel it is important to establish good relationships with them, to set realistic objectives, and to increase interprofessional interactions. We discuss these findings in relation to three issues that contribute to this sense of difficulty: the social distance between physicians and patients, the social determinants affecting patients’ health, and certain aspects of the health care system that impede the above-mentioned adjustments in medical practice. By exploring, social inequalities in health care access not through the experience of patients, but rather through the perspectives of the physicians who feel unable to protect them, this analysis highlights that the scope of action required to address such inequalities far exceeds physicians’ practice.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.217
GPT teacher head0.501
Teacher spread0.284 · 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

Citations14
Published2019
Admission routes2
Has abstractyes

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