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Record W2782695496 · doi:10.1002/ajcp.12225

Community Psychology and Community Mental Health: A Call for Reengagement

2018· article· en· W2782695496 on OpenAlexaff
Greg Townley, Molly Brown, John Sylvestre

Bibliographic record

VenueAmerican Journal of Community Psychology · 2018
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCommunity psychologyMental healthHealth psychologyTransformative learningPsychologyCommunity engagementParticipatory action researchPsychological interventionPublic relationsScholarshipMiddle Eastern Mental Health Issues & SyndromesCommunity-based participatory researchPublic healthSociologyMental illnessMedicinePsychiatrySocial psychologyPolitical scienceNursingPedagogy

Abstract

fetched live from OpenAlex

Community psychology is rooted in community mental health research and practice and has made important contributions to this field. Yet, in the decades since its inception, community psychology has reduced its focus on promoting mental health, well-being, and liberation of individuals with serious mental illnesses. This special issue endeavors to highlight current efforts in community mental health from our field and related disciplines and point to future directions for reengagement in this area. The issue includes 12 articles authored by diverse stakeholder groups. Following a review of the state of community mental health scholarship in the field's two primary journals since 1973, the remaining articles center on four thematic areas: (a) the community experience of individuals with serious mental illness; (b) the utility of a participatory and cross-cultural lens in our engagement with community mental health; (c) Housing First implementation, evaluation, and dissemination; and (d) emerging or under-examined topics. In reflection, we conclude with a series of challenges for community psychologists involved in future, transformative, movements in community mental health.

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)0.026
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0150.002
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.015
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 head0.215
GPT teacher head0.558
Teacher spread0.343 · 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 designNot applicable
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

Citations11
Published2018
Admission routes1
Has abstractyes

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