MétaCan
Menu
Back to cohort
Record W2142039149

Health Disparity in Saskatoon

2008· article· en· W2142039149 on OpenAlexaboutno aff
Mark Lemstra

Bibliographic record

VenueRePub (Erasmus University, Rotterdam) · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsAnxietySocioeconomic statusMoodPsychologyMental healthClinical psychologyDepressed moodPopulationPsychiatryMedicineEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

Introduction A majority of population based studies suggest prevalence of depressed mood and anxiety is most common during late adolescence to early adulthood. Mental health status has been linked previously to socioeconomic status in adults. The purpose of this systematic literature review is to clarify if socioeconomic status (SES) is a risk indicator of depressed mood or anxiety in youth between the ages of 10 to 15 years old. Methods We performed a systematic literature review to identify published or unpublished papers between January 1, 1980 and October 31, 2006 that reviewed depressed mood or anxiety by SES in youth aged 10-15 years. Results We found nine studies that fulfilled our inclusion criteria and passed the methodological quality review. The prevalence of depressed mood or anxiety was 2.49 times higher (95% CI2.33-2.67) in youth with low SES in comparison to youth with higher SES. Discussion The evidence suggests that low SES has an inverse association with the prevalence of depressed mood and anxiety in youth between the ages of 10 to 15 years old. Higher rates of depressed mood and anxiety among lower socioeconomic status youth may impact emotional development and limit future educational and occupational achievement. Conclusion Lower socioeconomic status is associated with higher rates of depressed mood and anxiety in youth.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.535
Threshold uncertainty score0.924

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.007
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.000

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 head0.032
GPT teacher head0.294
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), 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
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueRePub (Erasmus University, Rotterdam)Same topicHealth disparities and outcomesFrench-language works237,207