OP76 Hospital-based preventative health services for people experiencing homelessness: systematic review and narrative synthesis
Notice bibliographique
Résumé
Background Preventative health services, such as screening, vaccinations, and referrals to health and social services, improve health outcomes and reduce healthcare utilisation, costs, and inequities. People experiencing homelessness have significant unmet needs, but data are lacking on preventative health service provision. We aimed to review literature on hospital-based preventative health services for people experiencing homelessness. Methods We systematically searched MEDLINE, Embase, PsycINFO, HMIC, CINAHL, Web of Science, and The Cochrane Library. We hand-searched the bibliographies and citing references of included studies. We included experimental and observational quantitative studies involving preventative health services in emergency departments or inpatient hospital settings from 1999–2019. The population included adults experiencing homelessness in high income countries. We included outcomes for health, social factors, healthcare utilisation, and healthcare costs. We managed studies in Endnote and extracted data using a standardised spreadsheet. We assessed quality and bias using the ‘Quality Assessment Tool for Quantitative Studies’ and narratively synthesised findings. Results We identified 7935 articles from searches and reviewed 149 full text articles. Thirty-two met our eligibility criteria and were conducted in the USA (n=15), UK (n=9), Canada (n=4), and Australia (n=4). Sixteen studies were undertaken in emergency departments, 13 in inpatient wards, and 3 were conducted in both settings. We identified eight intervention categories: 1) homelessness screening, 2) case management, 3) screening, treatment initiation and referrals, 4) vaccinations, 5) discharge planning, 6) assistance with social needs, 7) pharmacological treatment, and 8) psychosocial services. Most studies described multi-component interventions. Results showed improvements in housing status, mental health, quality of life, and uptake of vaccinations and screening. Some studies reported successful integration with follow-up services, while others reported poor rates of onward care. Studies tended to report reductions in unplanned healthcare utilisation and costs, though not consistently. None showed harms. The overall strength of the evidence was weak to moderate with few randomised controlled trials. Discussion Hospital-based preventative health services can improve housing status and health and may reduce unplanned healthcare utilisation and costs for people experiencing homelessness. Definitive data are lacking for effective integration across healthcare systems. Policy-makers and practitioners should consider providing hospital-based preventative services to tackle unmet needs and health inequities. Our study is limited by the lack of qualitative, grey literature, and non-English studies. Future research should investigate barriers and levers for successful implementation of hospital-based preventative health services and the integration of hospitals with primary care and other services.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,028 | 0,091 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,009 | 0,008 |
| Bibliométrie | 0,021 | 0,018 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».