A mixed-methods exploration on whether and how community health navigators impact the mental health of adults with chronic health conditions in ENCOMPASS
Notice bibliographique
Résumé
Background: Living with chronic health conditions (CHCs) can be distressing due to impacts on quality of life. People with CHCs are more susceptible to mental illness such as anxiety or depression. The Community Health Navigator (CHN) program being tested in the Enhancing Community Health Through Patient Navigation, Social Advocacy, and Social Support (ENCOMPASS) study helps patients living with CHCs address health burdens. CHNs are Community Health Workers who provide patient navigation, and address barriers to care. They support patients by facilitating communication with care providers, connecting them to resources, encouraging care plan adherence, providing advice, education, and emotional support, and helping carry out health-related goals. CHN support may address anxiety and depression, and this study is an opportunity to understand this impact. Purpose: To explore, using a convergent mixed-methods design, whether and how the CHN intervention impacts anxiety and depression of adult patients with CHCs enrolled in the ENCOMPASS study at a Primary Care Network in Calgary, Alberta. Methods: The quantitative portion of this study used anxiety and depression scores from the Generalized Anxiety Disorder Scale (GAD-7) and Patient Health Questionnaire (PHQ-9), respectively, administered at baseline, 6- and 12-months. The scores of CHN intervention and control patients were compared at 6- and 12-months using Analysis of Covariance (ANCOVA). The qualitative portion used a qualitative descriptive approach where inductive content analysis was used to analyze transcripts from semi-structured interviews with CHNs and patients, and CHN case notes. The analysis sought to explain the quantitative results and explore how CHNs addressed patients’ mental health concerns. Results: Out of 183 patients enrolled in the ENCOMPASS study, between 140 and 149 patients were included in the quantitative analyses, depending on data completeness for each outcome. After data transformation to address assumptions violations, there were no significant differences in anxiety and depression scores between intervention and usual care (control) patients at either 6 or 12 months after enrolment (p > 0.05). The qualitative analysis provided insight into these results, revealing that CHNs faced challenges that hindered their ability to address patients’ mental health, such as a lack of training and patient discomfort towards mental health work. There were also challenges that patients faced in addressing their mental health, such as a lack of relevant mental health resources, and pressures from the COVID-19 pandemic. Despite not detecting a quantifiable effect on outcomes, patients reported that working with a CHN benefited their mental health, and improved well-being and feelings of being cared for. Conclusion: The degree of consistency between the qualitative and quantitative results was mixed. The combination of challenges faced by CHNs and patients might explain why CHN support did not have a significant impact on anxiety and depression symptoms scores. However, patient reports of benefit to mental wellbeing and psychosocial health indicate that the CHN program may improve mental health in ways that were not objectively measured. This study evaluated the impact that CHNs had on anxiety and depression, and contributes to the growing knowledge on the impact of patient navigation, and the mental health of patients with CHCs in Canada.
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 | 0,079 | 0,059 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».