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Record W3002277371

Toronto Community Health Centres: Environmental Perspectives

2019· article· en· W3002277371 on OpenAlexaboutno aff
Kaila Wong

Bibliographic record

VenueYorkSpace (York University) · 2019
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental planningGeographyEnvironmental healthMedicine
DOInot available

Abstract

fetched live from OpenAlex

Although environmental health and justice concerns have long since been recognized as determinant of human health, environmental health is not frequently viewed as a primary health concern within healthcare centres. This exploratory paper examines how Community Health Centres (CHCs) in Toronto are integrating environmental health and/or justice perspectives into their health promotion initiatives, whether they have changed over the years, and why. Two case studies, the South Riverdale Community Health Centre and the Davenport-Perth Neighbourhood Community Health Centre, were examined through review of academic literature, existing public documentation, and interviews as a means to provide a sample of CHCs. Analysis yielded the discovery that though environmental health perspectives are still being integrated into health promotion methods and strategies, many uncontrollable factors such as funding, community interest, and pre-existing social concerns influence whether environmental health programming can be delivered. Rather, it was determined that the majority of current environmental health perspectives are mainly integrated into other forms of health promotion, such as physical or mental health. General recommendations are made at the end of the paper addressed to the two case studies, all CHCs, and the healthcare system, for improving overall health outcomes of communities through CHCs utilization.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.029
GPT teacher head0.316
Teacher spread0.287 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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