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Record W2022905081 · doi:10.3148/73.3.2012.122

Patient Reports of: Lifestyle Advice in Primary Care

2012· article· en· W2022905081 on OpenAlexaffvenueabout
Paula Brauer, Lee Anne Sergeant, Bridget Davidson, R. Goy, Linda Dietrich

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2012
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsBarrie Urology GroupUniversity of Guelph
Fundersnot available
KeywordsPrimary careAdvice (programming)MedicineFamily medicineComputer science

Abstract

fetched live from OpenAlex

PURPOSE: Patients' perceptions of preventive lifestyle in primary care practice were examined. METHODS: Practice was assessed with a modified version of the Primary Care Assessment Survey (PCAS). This was mailed to random samples of patients twice, using practice mailing lists from three Ontario Family Health Networks (FHNs). Family Health Networks are physician-based group practices, with additional nurse-led telephone advisory services to provide care 24 hours a day, seven days a week. The PCAS questionnaire consisted of nine scales (ranging from 0 to 100). For preventive counselling, additional questions on diet and exercise counselling were included to determine how the physician delivered the intervention. RESULTS: Of the 2184 survey questionnaires mailed to patients, 22% were undeliverable. The response rate was 62% at valid addresses (49% of all mailed questionnaires). Of the nine scales, scores (± standard deviation) for preventive counselling were lowest at 33 ± 25. In particular, rates of diet (37%) and exercise (24%) counselling were low in the FHNs. For most other aspects of primary care services, patients generally rated FHNs highly. The majority of patients advised about diet and exercise were given verbal advice or pamphlets. CONCLUSIONS: In these primary health care organizations, considerable room exists for increased preventive counselling, especially about diet and exercise.

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)0.009
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.095
GPT teacher head0.482
Teacher spread0.388 · 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

Citations19
Published2012
Admission routes3
Has abstractyes

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