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Record W3107496286 · doi:10.12927/hcq.2020.26340

Mental Health and Addictions System Performance in Ontario: An Updated Scorecard, 2009–2017

2020· article· en· W3107496286 on OpenAlexaffvenueabout
Maria Chiu, Astrid Guttmann, Paul Kurdyak

Bibliographic record

VenueHealthcare Quarterly · 2020
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsCentre for Addiction and Mental HealthHospital for Sick Children
Fundersnot available
KeywordsBalanced scorecardMental healthAddictionHarmMedicinePsychiatryMedical emergencyPsychologyBusinessMarketingSocial psychology

Abstract

fetched live from OpenAlex

Scorecards, particularly those that report on health system performance over time, can shed light on issues related to access and quality. In this updated 2020 Mental Health and Addictions Scorecard, we report on a number of indicators between 2009 and 2017. In general, we found that the performance of the mental health and addictions health system did not improve substantially over time. Among the many findings, over the past decade, suicide rates have not declined and rates of emergency department visits for deliberate self-harm have continued to rise. The highest rates of deliberate self-harm and the greatest rise over time in overall mental health and addictions-related outpatient visits, emergency department visits and hospitalizations were experienced by individuals aged 14-24 years. There continues to be a growing use of mental health services in outpatient settings, with the majority of care provided by primary care physicians. We also observed a slight decrease over time in the proportion of individuals who had no physician-delivered mental health care prior to presenting to the emergency department, which suggests an improvement in access over time.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.233
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.051
GPT teacher head0.321
Teacher spread0.269 · 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

Citations16
Published2020
Admission routes3
Has abstractyes

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