Notice bibliographique
Résumé
Until recently, there was little empirical evidence regarding the most effective intervention for a very vulnerable population, adults who are homeless with a mental illness. Many programs existed, but they were supported mainly by descriptive studies or nonexperimental designs. This began to change about 15 years ago, following the introduction of a novel and somewhat controversial program, Pathways to HF.1 Unlike more traditional housing approaches that first require clients to engage in treatment and stop abusing drugs and alcohol, the HF approach, true to its name, offers people who are homeless their own scattered-site apartments, without any preconditions. Since that time, there have been numerous RCTs demonstrating its effectiveness and cost-effectiveness. The latest, and by far the largest, trial was the AH–CS study, funded by Health Canada through the Mental Health Commission of Canada.2 This study involved 2148 people in 5 cities across Canada, randomized to receive either HF plus either assertive community treatment (for those with high needs) or intensive case management (for those with moderate needs), rather than TAU. In this issue of the journal, 2 articles3,4 review the literature regarding the HF approach. The first paper, by Aubry et al,3 defines the original program model and summarizes the findings about implementation and effectiveness, with an emphasis on those from the AH–CS project. Despite dire warnings by an external reviewer at the start of that study that very large projects rarely tap the richness of their data or publish enough papers to justify their expense, AH–CS has already resulted in 80 papers (and counting). Consequently, it is useful to have one article summarizing the major findings. The second paper, by Ly and Latimer,4 is both narrower in scope, focusing only on the economic findings, and broader, in that it marshals the results from other studies of the more generic HF approach. There are numerous lessons to be learned from these reviews. The first, and most important, is that HF is very successful, most especially regarding the primary outcome of enabling people with a mental illness who are homeless to find and maintain stable housing for an extended period of time. The second conclusion is the necessity to use an RCT design in evaluating efficacy and effectiveness of complex interventions. Ly and Latimer’s review found that studies that used a pre–post design reported an overall decrease in costs with HF, whereas RCTs showed a net increase in costs, except for people who were the highest users of services. Other papers coming out of the HF–CS study have shown that, while those in the intervention group demonstrated improvements, in areas such as mental health symptoms and substance abuse problems, this was paralleled by equivalent gains in the TAU group.5,6 Not having a control group would have exaggerated the extent to which HF has a beneficial result. A third conclusion is that the impact of evidence on policy is enhanced by using rigorous designs that include economic results and are combined with participatory approaches to knowledge translation. The remarkable receptiveness of the Canadian government to the findings of the AH–CS study that are described in the Aubry et al3 review were achieved through a combination of good science and an extensive communication strategy that addressed the cost offsets, a central preoccupation of decision makers. A final lesson relates to the impact of evidence on practice, which is enhanced by a multi-site, mixed methods approach that included rich qualitative data about the experience of the providers and participants in various settings. The technical assistance and training efforts that are under way across Canada rely heavily on the credibility and expertise of people who were actively involved in the demonstration project. The impact of evidence about implementation and outcomes on policy and practice always involves myriad factors beyond the results themselves.7 Still, the quality and quantity of the research matters. These review articles3,4 provide a helpful summary of the existing evidence to inform decisions about how best to support homeless adults with a mental illness to find a home.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».