Barriers, facilitators and solutions to the care of people experiencing homelessness with traumatic brain injury in Quebec, Canada: clinicians’ and concerned parties’ perspectives
Notice bibliographique
Résumé
People experiencing homelessness have disproportionately high rates of traumatic brain injury (TBI), yet services remain inaccessible or poorly adapted to their needs. Limited research has explored the barriers, facilitators and potential solutions to improve healthcare for this population. The objectives were to identify the individual- and environment-level barriers to healthcare for people experiencing homelessness who have sustained a TBI, identify the environment-level facilitators to care for this population, and identify potential solutions to improve care. A qualitative descriptive study was conducted and four focus groups were held (n = 20), consisting of healthcare professionals (n = 10), community workers (n = 6), and managers from both sectors (n = 4). Data were analyzed using Braun and Clarke’s thematic analysis approach. Participants reported: (1) healthcare structures misaligned with the realities of people experiencing homelessness; (2) reduced trust in health services by people experiencing homelessness; (3) reliance on overburdened community organizations lacking TBI expertise; and (4) transforming care requires cross-sector collaborations and rethinking current healthcare delivery to provide more flexible TBI services. Healthcare for this population is not optimal and fails to meet their needs. Implementing low-threshold service models, fostering collaboration, and providing targeted training could significantly improve TBI care for this population. Possible solutions to the current siloed approach to care for people experiencing homelessness (PEH) include the development of mobile health services that include traumatic brain injury (TBI)-specific expertise, and housing services adapted to the needs of people experiencing homelessness living with cognitive and behavioral impairments.Cross-sector collaborative training and initiatives have shown potential for other populations with complex health needs and could be adapted to bridge the gap between TBI-specific and homelessness-specific services.Healthcare policies need to include access to healthcare and rehabilitation services for underserved populations to support transition from homelessness to more humane and adequate housing situations. Possible solutions to the current siloed approach to care for people experiencing homelessness (PEH) include the development of mobile health services that include traumatic brain injury (TBI)-specific expertise, and housing services adapted to the needs of people experiencing homelessness living with cognitive and behavioral impairments. Cross-sector collaborative training and initiatives have shown potential for other populations with complex health needs and could be adapted to bridge the gap between TBI-specific and homelessness-specific services. Healthcare policies need to include access to healthcare and rehabilitation services for underserved populations to support transition from homelessness to more humane and adequate housing situations.
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 | 0,006 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,026 | 0,008 |
| Communication savante | 0,006 | 0,002 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».