Information for mental health systems: an instrument for policy-making and system service quality
Notice bibliographique
Résumé
AIMS: Information is crucial in mental healthcare, yet it remains undervalued by stakeholders. Its absence undermines rationality in planning, makes it difficult to monitor service quality improvement, impedes accountability and human rights monitoring. For international organizations (e.g., WHO, OECD), information is indispensable for achieving better outcomes in mental health policies, services and programs. This article reviews the importance of developing system level information with reference to inputs, processes and outputs, analyzes available tools for collecting and summarizing information, highlights the various goals of information gathering, discusses implementation issues and charts the way forward. METHODS: Relevant publications and research were consulted, including WHO studies that purport to promote the use of information systems to upgrade mental health care in high- and low-middle income countries. RESULTS: Studies have shown that once information has been collected by relevant systems and analyzed through indicator schemes, it can be put to many uses. Monitoring mental health services, represents a first step in using information. In addition, studies have noted that information is a prime resource in many other areas such as evaluation of quality of care against evidence based standards of care. Services data may support health services research where it is possible to link mental health data with other health and non-health databases. Information systems are required to carefully monitor involuntary admissions, restrain and seclusion, to reduce human rights violations in care facilities. Information has been also found useful for policy makers, to monitor the implementation of policies, to evaluate their impact, to rationally allocate funding and to create new financing models. CONCLUSIONS: Despite its manifold applications, Information systems currently face many problems such as incomplete recording, poor data quality, lack of timely reporting and feedback, and limited application of information. Corrective action is needed to upgrade data collection in outpatient facilities, to improve data quality, to establish clear rules and norms, to access adequate information technology equipment and to train health care personnel in data collection. Moreover, it is necessary to shift from mere administrative data collection to analysis, dissemination and use by relevant stakeholders and to develop a "culture of information" to dismantle the culture of intuition and mere tradition. Clinical directors, mental health managers, patient and family representatives, as well as politicians should be educated to operate with information and not just intuition.
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 | 0,009 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».