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Record W2767868508 · doi:10.28984/drhj.v1i0.38

Protecting the mental health of Ontario seniors

2017· article· en· W2767868508 on OpenAlexafffundvenueabout
Phyllis Montgomery, Parveen Nangia, Sharolyn Mossy, Darrin Pye

Bibliographic record

VenueDiversity of Research in Health Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsLaurentian University
FundersHealth Canada
KeywordsMental healthGerontologyMarital statusHabilitationFlourishingPsychologyMedicinePopulationEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

Background: Chronic disease management has been the emphasis of research with elderly populations. Building upon this work, there is a need to examine protective practices and supports for the mental health of older adults. There are no Canadian empirical studies that examine those factors in relation to positive mental health.
 Aim: This study identified associations between protective factors and self-rated mental health for senior, community-dwelling Ontarians.
 Methods: A secondary analysis was undertaken to examine a subset of variables from the population-based Canadian Community Health Survey (2012) data. Statistical analysis correlated a range of extracted individual, social, and environmental variables for two major age groups of Ontarian seniors (N = 6,121), those aged 65 to 79, and those 80 years and older.
 Results: Positive mental health was significantly associated with marital status, co-habilitation, income, perceived health, life stress, and health behaviours. This grouping of personal and social characteristics lost significance for positive mental health among seniors aged 80 years and older. Regardless of the seniors’ age, flourishing mental health was predicated upon physical activity, positive self-rated physical health, and finally, limited life stress.
 Conclusion: Based on provincial data, trends in self-rated positive mental health suggest that key factors ought to be integrated into the delivery of coordinated and interdisciplinary services to protect the mental health of seniors.

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)0.037
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0370.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0180.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.256
GPT teacher head0.501
Teacher spread0.245 · 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
Published2017
Admission routes4
Has abstractyes

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