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Record W2115050652 · doi:10.1017/s1121189x0000796x

Systematic review of the role of service users as researchers in mental health studies

2005· review· en· W2115050652 on OpenAlexaboutno aff
Giovanni E. Salvi, Julia Jones, Mirella Ruggeri

Bibliographic record

VenueEpidemiology and Psychiatric Sciences · 2005
Typereview
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthQualitative researchService (business)Mental health serviceSystematic reviewInclusion (mineral)Process (computing)PsychologyMedicineMEDLINEComputer scienceBusinessPsychiatryPolitical scienceSociologySocial psychologyMarketingSocial science

Abstract

fetched live from OpenAlex

AIMS: Service user involvement in mental health service development and research is becoming more common in countries like the UK, USA and Canada. This systematic review of the international scientific literature has been carried out to assess the stage of development of mental health service users involvement in research. METHOD: Systematic review of any research project actively involving service users in any part of the research process. RESULTS: Thirty-five studies met the inclusion and exclusion criteria and were included in the systematic review. Nine studies used quantitative techniques, 24 used qualitative techniques and two studies used both quantitative and qualitative techniques. While three studies were user-led, in three other studies the users were simply consulted but did not have any active role in the research. The remaining 29 studies were based on a collaboration between service users and professional researchers. CONCLUSIONS: The involvement of mental health service users in the research process is feasible both in quantitative and qualitative research studies. The involvement of service users in research has a number of benefits; such research requires more accurate planning and more time than the traditional research.

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)0.031
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.122
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0310.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.647
GPT teacher head0.624
Teacher spread0.023 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations8
Published2005
Admission routes1
Has abstractyes

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