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Record W2119813461 · doi:10.1017/s0714980813000597

Negotiating Vulnerabilities: How Older Adults with Multiple Chronic Conditions Interact with Physicians

2013· article· fr· W2119813461 on OpenAlexaff
Laura Hurd Clarke, Erica Bennett, Alexandra Korotchenko

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2013
Typearticle
Languagefr
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMultiple Chronic ConditionsTrustworthinessNegotiationMedicineChronic diseaseHealth careFamily medicineHealth professionalsPsychologyNursingSocial psychology

Abstract

fetched live from OpenAlex

The literature on patient-physician interactions has largely ignored the perspectives of older adults with multiple morbidities. Featuring in-depth interview data from 16 men and 19 women with an average of six chronic conditions, this study focused on how participants perceived and experienced the care provided by their primary care physicians. Participants suggested that physicians caring for patients with multiple chronic conditions should be thorough, amenable to gate keeping, trustworthy, and open to different decision-making styles. However, many study participants perceived that they received inadequate care due to the personal failings of their physicians, constraints of medical consultations, and societal ageism. Consequently, many of the participants, especially the women, employed various strategies to maximize the care they received and manage their physicians' impressions of them as worthy patients. Our findings suggest that elderly patients with multiple morbidities perceive that their health needs are not being adequately met.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0050.005
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.273
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), 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

Citations58
Published2013
Admission routes1
Has abstractyes

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