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Record W3043126822 · doi:10.7870/cjcmh-2020-006

Accessing and Utilizing One’s City Space: The Role of Specialized Community Mental Health Teams in Brazil and Canada

2020· article· en· W3043126822 on OpenAlexafffundvenueabout
Émmanuelle Khoury, Isabelle Ruelland

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de MontréalCollège Lionel Groulx
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMental healthSpace (punctuation)Mental health serviceEthnographyService providerService (business)Public relationsNursingPsychologySociologyMedicineBusinessPolitical scienceMarketingPsychiatryComputer science

Abstract

fetched live from OpenAlex

Community mental health programs have garnered significant attention during the last decade. In this paper we ask to what extent these programs impact the capacity of service users to move around in the city space. Drawing on case studies from ethnographic research conducted in Campinas (Brazil) and Montréal (Canada), which included semi-structured interviews with a total of 16 service users and 49 mental health professionals, we explore the significance of urban mobility as part of service users’ mental health recovery and service providers’ practice. Findings suggest that service providers play a key role in facilitating meaningful mobility in the city space.

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.416

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0260.008
Scholarly communication0.0060.002
Open science0.0030.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.208
GPT teacher head0.412
Teacher spread0.203 · 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 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

Citations1
Published2020
Admission routes4
Has abstractyes

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