Current Barriers to Accessing Mental Healthcare Resources for Inflammatory Arthritis Patients and How to Improve Support
Notice bibliographique
Résumé
Objectives To understand perspectives of inflammatory arthritis (IA) patients and healthcare providers on barriers to accessing mental healthcare resources and identify facilitators to better support patients at rheumatology clinic visits. Methods Semi-structured interviews and focus groups were conducted with patients, allied healthcare professionals, and rheumatologists until saturation of responses was reached. Patients with IA reporting varied levels of anxiety and depression were recruited from the Rheum4U Precision Health Registry using purposive sampling. Healthcare providers were from 2 rheumatology clinics in Calgary. Surveys with closed-ended questions were completed by patients on their use of mental healthcare resources and healthcare providers on supporting their patients with accessing resources. The sessions explored barriers to accessing mental healthcare resources and facilitators that could support patients. Inductive thematic analysis was used to code perceived barriers and facilitators to accessing mental healthcare resources. Results Six patients, 2 allied health professionals, 5 nurses, and 3 rheumatologists participated in semi-structured interviews and focus groups. Four patients reported having anxiety/depression and 3 said they would like to discuss their mental health at rheumatology clinic visits. Key findings included: Patient challenges: patients have trouble initiating conversations about their mental health. While a social worker can assist in navigating resources, some patients are unaware of this resource. Healthcare provider constraints and attitudes: healthcare providers indicated they lack the time and support staff to discuss mental health with their patients. While some healthcare providers expressed concern about not screening for their patients’ mental health, other providers believed addressing mental health concerns is out of scope. Systemic issues: healthcare providers discussed long wait times for mental health specialists and both patients and healthcare providers had difficulties navigating available resources. Supports needed: healthcare providers expressed a need for a ‘toolkit’ to better support their patients’ mental health and additional training on this topic. A Venn diagram illustrating barriers and facilitators perceived by patients, allied health professionals and rheumatologists is shown in Figure 1. Conclusion Patients and providers expressed lack of clarity about the availability of mental health resources. There is variability in provider attitudes toward addressing mental health concerns during rheumatology appointments. Both patients and providers identified systemic issues in accessing mental healthcare resources. Quick screening, support from allied health professionals at the clinic, and a quick reference guide that incorporates existing workflow could potentially help improve access to mental healthcare resources for patients with IA.
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 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».