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Record W7057060601

The impact of socio-economic status and interpersonal dimensions of care on attaining and maintaining healthy behaviours among primary care patients

2013· dissertation· en· W7057060601 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2013
Typedissertation
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsInterpersonal communicationRandom digit dialingLogistic regressionHealth carePopulationSocioeconomic statusInterpersonal relationshipDescriptive statistics
DOInot available

Abstract

fetched live from OpenAlex

Context: Attaining and maintaining health behaviours could significantly prevent and reduce the health burden associated with chronic illness. Given that family physicians are the first source of care for patients undifferentiated by disease or socio-demographics, the way they deliver care may play a significant role in improving population health. The premise underlying this research is that patient empowerment, effective interpersonal communication and patient-centered care can improve health behaviors among primary care patients, especially those coming from low socioeconomic groups. Objectives: 1) To determine whether the prevalence of healthy fruit/vegetable consumption, physical activity, alcohol intake and smoking differ systematically by socio-economic status for patients at one point in time. 2) To determine whether socio-economic status predicts the likelihood of attaining or maintaining healthy behavior after one year. 3) To determine whether patients' assessments of three interpersonal dimensions of care (physician empowerment, interpersonal communication skills and patient-centeredness) impact the likelihood of attaining or maintaining healthy behaviours after one year. 4) To determine whether the three interpersonal dimensions of care modify the effect of socio-economic status on attainment or maintenance of healthy behaviours. Methods: A cohort of 2456 patients, aged between 25-to-75 years, was recruited from waiting rooms of 12 clinics and by random digit dialing in four health networks in Quebec. Using annual self-administered questionnaires, socio-demographic information, healthcare experience, and health behaviors were elicited. Using current guidelines, we classified health behaviors into "meeting target" or being at "risky behavior". A cluster analysis was used to classify the study population into four socio-economic groups. Descriptive statistics and logistic regression analyses were used to determine those characteristics associated with the prevalence as well as changes in health behaviours. Results: There is a statistically significant gradient between socio-economic status and the prevalence of fruit and vegetables consumption and smoking with healthy behavior decreasing systematically with each decrease in socio-economic status. The highest socioeconomic group is most likely to maintain adequate physical activity after one year (OR 2.3, 95% CI: 1.2 – 4.2) and the lowest socio-economic group to maintain non-risky alcohol consumption (OR 0.3. 95% CI:0.2 – 0.7) but socio-economic status did not impact adoption of any behaviour. Of the interpersonal dimensions of care, only higher assessments of patient empowerment predict maintaining but only of healthy fruit and vegetable (OR 1.2, 95% CI: 1.0 – 1.5). Despite the gradient between assessment of empowerment and socio-economic status, the impact of empowerment does not vary statistically significantly by socio-economic status. Conclusion: In general, people from low socio-economic demographic have higher rates of smoking, lower rates of fruit and vegetable consumption and are less likely to maintain adequate levels of physical activity. Generic empowerment actions may have a positive impact on the maintenance of healthy behaviours, and every effort should be made to ensure that all socio-economic groups benefit from physicians' empowerment actions

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.802
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.007
GPT teacher head0.238
Teacher spread0.231 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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