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Record W2803624746 · doi:10.1186/s12961-018-0305-1

Problems maintaining collaborative approaches with excluded populations in a randomised control trial: lessons learned implementing Housing First in France

2018· article· en· W2803624746 on OpenAlexaboutno aff
Pauline Rhenter, Aurélie Tinland, Julien Grard, Christian Laval, Jean Mantovani, Delphine Moreau, Benjamin Vidaud, Tim Greacen, Pascal Auquier, Vincent Girard

Bibliographic record

VenueHealth Research Policy and Systems · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
FundersFondation de France
KeywordsHousing FirstThematic analysisPoliticsPublic relationsGrey literaturePublic administrationParticipant observationSociologyPolitical scienceQualitative researchMental healthMedicineMEDLINESocial sciencePsychiatryMental illness

Abstract

fetched live from OpenAlex

BACKGROUND: In 2006, a local collective combating homelessness set up an 'experimental squat' in an abandoned building in Marseille, France's second largest city. They envisioned the squat as an alternative to conventional health and social services for individuals experiencing long-term homelessness and severe psychiatric disorders. Building on what they learned from the squat, some then joined a larger coalition that succeeded in convincing national government decision-makers to develop a scientific, intervention-based programme based on the Housing First model. This article analyses the political process through which social movement activism gave way to support for a state-funded programme for homeless people with mental disorders. METHODS: A qualitative study of this political process was conducted between 2006 and 2014, using a hybrid theoretical perspective that combines attention to both top-down and bottom-up actions with a modified Advocacy Coalition Framework. In addition to document analysis of published and grey literature linked to the policy process, researchers drew on participant observation and observant participation of the political process. Data analysis consisted primarily of a thematic analysis of field-notes and semi-structured interviews with 65 relevant actors. RESULTS: A coalition of local activists, state officials and national service providers transformed knowledge about a local innovation (an experimental therapeutic squat) into the rationale for a national, scientifically based project consisting of a randomised controlled trial of four state-supported Housing First sites, costing several million euros. The coalition's strategy was two-pronged, namely to defend a social cause (the right to housing) and to promote a scientifically validated means of realising positive outcomes (housing tenure) and cost-effectiveness (reduced hospitalisation costs). CONCLUSION: Activists' self-agency, especially that of making themselves audible to public authorities, was enhanced by the coalition's ability to seize 'windows of opportunities' to their advantage. However, in contrast to the United States and Canadian Housing First contexts, which are driven by implementation science and related approaches, it was grassroots activists who promoted a scientific-technical approach among government officials unfamiliar with evidence-based practices in France. The windows of opportunity nevertheless failed to attract participation of those most in need of housing, raising the question of whether and how marginalised and/or subordinate groups can be integrated into collaborative research when a social movement-driven innovation turns into a scientific approach. TRIAL REGISTRATION: The current clinical trial number is NCT01570712 . Registered July 17, 2011. First patient enrolled August 18, 2011.

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)0.402
metaresearch head score (Gemma)0.369
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.737

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4020.369
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0020.002
Science and technology studies0.0030.007
Scholarly communication0.0060.008
Open science0.0050.005
Research integrity0.0110.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.675
GPT teacher head0.594
Teacher spread0.081 · 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.

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

Citations12
Published2018
Admission routes1
Has abstractyes

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