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Enregistrement W4377042342 · doi:10.1002/cl2.1329

Exploring the effect of case management in homelessness per components: A systematic review of effectiveness and implementation, with meta‐analysis and thematic synthesis

2023· review· en· W4377042342 sur OpenAlexaboutno aff
Alison Weightman, Mark Kelson, Ian Thomas, Mala Mann, Lydia Searchfield, Simone Willis, Ben Hannigan, Robin Smith, Rhiannon Cordiner

Notice bibliographique

RevueCampbell Systematic Reviews · 2023
Typereview
Langueen
DomaineHealth Professions
ThématiqueHomelessness and Social Issues
Établissements canadiensnon disponible
Organismes subventionnairesHeriot-Watt University
Mots-clésPsychological interventionThematic analysisNonprobability samplingSystematic reviewIntervention (counseling)Mental healthLife expectancyPsychologyGrey literatureMeta-analysisPublic healthMEDLINEGerontologyMedicineNursingQualitative researchEnvironmental healthPolitical sciencePsychiatrySociologyPopulationSocial science

Résumé

récupéré en direct d'OpenAlex

Background: Adequate housing is a basic human right. The many millions of people experiencing homelessness (PEH) have a lower life expectancy and more physical and mental health problems. Practical and effective interventions to provide appropriate housing are a public health priority. Objectives: To summarise the best available evidence relating to the components of case-management interventions for PEH via a mixed methods review that explored both the effectiveness of interventions and factors that may influence its impact. Search Methods: We searched 10 bibliographic databases from 1990 to March 2021. We also included studies from Campbell Collaboration Evidence and Gap Maps and searched 28 web sites. Reference lists of included papers and systematic reviews were examined and experts contacted for additional studies. Selection Criteria: We included all randomised and non-randomised study designs exploring case management interventions where a comparison group was used. The primary outcome of interest was homelessness. Secondary outcomes included health, wellbeing, employment and costs. We also included all studies where data were collected on views and experiences that may impact on implementation. Data Collection and Analysis: We assessed risk of bias using tools developed by the Campbell Collaboration. We conducted meta-analyses of the intervention studies where possible and carried out a framework synthesis of a set of implementation studies identified by purposive sampling to represent the most 'rich' and 'thick' data. Main Results: We included 64 intervention studies and 41 implementation studies. The evidence base was dominated by studies from the USA and Canada. Participants were largely (though not exclusively) people who were literally homeless, that is, living on the streets or in shelters, and who had additional support needs. Many studies were assessed as having a medium or high risk of bias. However, there was some consistency in outcomes across studies that improved confidence in the main findings. Case Management and Housing Outcomes: = 0.03) at ≥12 months. There was not enough evidence to compare the above approaches with standard case management within the meta-analyses. A narrative comparison across all studies was inconclusive, though suggestive of a trend in favour of more intensive approaches. Case Management and Mental Health Outcomes: = 0.817). Case Management and Other Outcomes: < 0.01) but was not statistically significantly different for substance use outcomes, physical health, and employment. Case Management Components: = 0.02). There was not enough evidence from meta-analysis to assess whether the case manager should have a professional qualification, or if frequency of contact, case manager availability or conditionality (barriers due to conditions attached to service provision) influenced outcomes. However, the main theme from implementation studies concerned barriers where conditions were attached to services. Characteristics of Persons Experiencing Homelessness: = 0.3. The Broader Context of Delivery of Case Management Programmes: Other major themes from the implementation studies included the importance of interagency partnership; provision for non-housing support and training needs of PEH (such as independent living skills), intensive community support following the move to new housing; emotional support and training needs of case managers; and an emphasis on housing safety, security and choice. Cost Effectiveness: The 12 studies with cost data provided contrasting results and no clear conclusions. Some case management costs may be largely off-set by reductions in the use of other services. Cost estimates from three North American studies were $45-52 for each additional day housed. Authors' Conclusions: Case management interventions improve housing outcomes for PEH with one or more additional support needs, with more intense interventions leading to greater benefits. Those with greater support needs may gain greater benefit. There is also evidence for improvements to capabilities and wellbeing. Current approaches do not appear to lead to mental health benefits. In terms of case management components, there is evidence in support of a team approach and in-person meetings and, from the implementation evidence, that conditions associated with service provision should be minimised. The approach within Housing First could explain the finding that overall benefits may be greater than for other types of case management. Four of its principles were identified as key themes within the implementation studies: No conditionality, offer choice, provide an individualised approach and support community building. Recommendations for further research include an expansion of the research base outside North America and further exploration of case management components and intervention cost-effectiveness.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,074
score de la tête « metaresearch » (Gemma)0,180
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,074
Score d'incertitude au seuil0,394

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0740,180
Méta-épidémiologie (sens strict)0,0040,003
Méta-épidémiologie (sens large)0,0280,040
Bibliométrie0,0210,018
Études des sciences et des technologies0,0010,002
Communication savante0,0080,006
Science ouverte0,0040,005
Intégrité de la recherche0,0030,003
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,280
Tête enseignante GPT0,475
Écart entre enseignants0,196 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations14
Publié2023
Routes d'admission1
Résumé présentoui

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