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Record W2100619589 · doi:10.1177/10253823050120020108

What makes mental health promotion effective?

2005· article· en· W2100619589 on OpenAlexaboutno aff
Eva Jané‐Llopis, Margaret M. Barry

Bibliographic record

VenuePromotion & Education · 2005
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

When designing a comprehensive strategy for mental health promotion, as that called for in the European WHO Action Plan for Mental Health (WHO, 2005), one possible effective framework for such a strategy is to take a settings approach. The Ottawa Charter (WHO, 1986) for health promotion emphasises a settings-based approach in creating supportive environments for health, as reflected in the statement that 'health is created and lived by people within the settings of their everyday life; where they learn, work, play and love..'. An overview of effective mental health promotion programmes across different settings has been presented by Jané-Llopis and colleagues (Jané-Llopis, Barry, Hosman and Patel, 2005) in this volume, and in other recent reviews (WHO, 2004a; WHO, 2004b). The following section describes why the home, the school, the workplace and the community are four crucial settings for intervention, and describes a set of health and mental health determinants that are addressed through interventions in these settings.

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)0.028
metaresearch head score (Gemma)0.067
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.067
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0040.002
Science and technology studies0.0040.008
Scholarly communication0.0140.012
Open science0.0020.005
Research integrity0.0100.008
Insufficient payload (model declined to judge)0.0100.003

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 head0.066
GPT teacher head0.468
Teacher spread0.402 · 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 designTheoretical or conceptual
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

Citations93
Published2005
Admission routes1
Has abstractyes

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