Current needs for the improved management of depressive disorder in community healthcare centres, Shenzhen, China: a view from primary care medical leaders
Notice bibliographique
Résumé
BACKGROUND: The prevalence of depressive disorder in Shenzhen is higher than for any other city in China. Despite national health system reform to integrate mental health into primary care, the majority of depression cases continue to go unrecognized and untreated. Qualitative research was conducted with primary care medical leaders to describe the current clinical practice of depressive disorder in community healthcare centres (CHC) in Shenzhen and to explore the participants' perceptions of psychological, organizational and societal barriers and enablers to current practice with a view to identifying current needs for the improved care of depressive disorder in the community. METHODS: Seventeen semi-structured, audio-recorded interviews (approx. 1 h long) were conducted in Melbourne (n = 7) and Shenzhen (n = 10) with a convenience sample of primary care medical leaders who currently work in community healthcare centres (CHC) in Shenzhen and completed any one of the 3-month long, Melbourne-based, "Monash-Shenzhen Primary Healthcare Leaders Programs" conducted between 2015 and 2017. The interview guide was developed using the Theoretical Domain's Framework (TDF) and a directed content analysis (using Nvivo 11 software) was performed using English translations. RESULTS: Despite primary care medical leaders being aware of a mental health treatment gap and the benefits of early depression care for community wellbeing, depressive disorder was not perceived as a treatment priority in CHCs. Instead, hospital specialists were identified as holding primary responsibility for formal diagnosis and treatment initiation with primary care doctors providing early assessment and basic health education. Current needs for improved depression care included: (i) Improved professional development for primary care doctors with better access to diagnostic guidelines and tools, case-sharing and improved connection with mentors to overcome current low levels of treatment confidence. (ii) An improved consulting environment (e.g. allocated mental health resource; longer and private consultations; developed medical referral system; better access to antidepressants) which embraces mental health initiatives (e.g. development of mental health departments in local hospitals; future use of e-mental health; reimbursement for patients; doctors' incentives). (iii) Improved health literacy to overcome substantive mental health stigma in society and specific stigma directed towards the only public psychiatric hospital. CONCLUSIONS: Whilst a multi-faceted approach is needed to improve depression care in community health centres in Shenzhen, this study highlights how appropriate mental health training is central to developing a robust work-force which can act as key agents in national healthcare reform. The cultural adaption of the depression component of the World Health Organisation's mental health gap intervention guide (mhGAP-IG.v2) could provide primary care doctors with a future training tool to develop their assessment skills and treatment confidence.
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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 | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».