MétaCan
Menu
Back to cohort
Record W2043619050 · doi:10.1155/2014/458184

A Web-Based Survey of Residents’ Views on Advocating with Patients for a Healthy Built Environment in Canada

2014· article· en· W2043619050 on OpenAlexaffabout
Matthew Cruickshank, Marcus Law

Bibliographic record

VenueInternational Journal of Family Medicine · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsCanada Research ChairsUniversity of New BrunswickUniversity of Toronto
Fundersnot available
KeywordsMedicineWorld Wide WebWeb surveyData scienceFamily medicineComputer science

Abstract

fetched live from OpenAlex

Purpose. To determine family medicine residents' perceived knowledge and attitudes towards the built environment and their responsibility for health advocacy and to identify their perceived educational needs and barriers to patient education and advocacy. Methods. A web-based survey was conducted in Canada with University of Toronto family medicine residents. Data were analyzed descriptively. Results. 93% agreed or strongly agreed that built environment significantly impacts health. 64% thought educating patients on built environment is effective disease prevention; 52% considered this a role of family physicians. 78% reported that advocacy for built environment is effective disease prevention; 56% perceived this to be the family physician's role. 59% reported being knowledgeable to discuss how a patient's environment may affect his/her health; 35% reported being knowledgeable to participate in community discussions on built environment. 78% thought education would help with integration into practice. Inadequate time (92%), knowledge (73%), and remuneration (54%) were barriers. Conclusions. While residents perceived value in education and advocacy as disease prevention strategies and acknowledged the importance of a healthy built environment, they did not consider advocacy towards this the family physician's role. Barrier reduction and medical education may contribute to improved advocacy, ultimately improving physical activity levels and patient health outcomes.

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)0.005
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.474
GPT teacher head0.588
Teacher spread0.114 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Family MedicineSame topicHealth Policy Implementation ScienceFrench-language works237,207