Personal narratives of learning self‐management: Lessons for practice based on experiences of people with serious mental illness
Notice bibliographique
Résumé
INTRODUCTION: Clinicians are challenged to deliver self-management interventions in recovery-oriented services for individuals living with serious mental illnesses. Little is known about how people learn self-management skills and questions remain about how best to deliver support. To offer insights for delivery, this research describes the lived experiences of learning self-management and the meaning of those experiences within recovery journeys and the context of health-care delivery. METHODS: Design followed van Manen's approach of phenomenology through an occupational therapist's lens. Using purposeful criterion sampling until saturation, 25 adults with psychosis experiences (8-40 years) from six community-based specialised mental health programs were interviewed. Conceptual maps were cocreated depicting key learning experiences, intersections with services, and recommendations. Data reduction, reconstruction and explication of meaning occurred concurrently, and multiple strategies were used to transparently support an open, iterative, reflexive process. FINDINGS: Participants described eight essential tasks to live well, learned often serendipitously, taking up to 15-30 years to find the right combination of supports and self-management strategies to achieve what they felt was a life of quality. Self-management needs were not routinely addressed by services and extended beyond illness or crisis management while participants grappled with emotions, self-concept, relationships, and occupational issues. Participants asked providers to "teach us to teach ourselves"; "invite clients" to the decision table; and deal directly with emotions of fear, shame, and trust with respect to self and relationships. Findings challenge conventional conceptualisations of self-management to consider clients living interdependent lives with tasks performed in context, dynamically influenced by complex personal, socio-ecological relationships. CONCLUSIONS: Participants' narratives compel increasing access to strategic personalised self-management learning opportunities as an effort to shorten the prolonged recovery paths. Findings offer ways providers can understand and address eight self-management learning tasks from the perspective of lived experiences. Self-management was enmeshed with recovery, health, and building a life.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 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) | 0,001 | 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 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 ».