Notice bibliographique
Résumé
Ten years ago, Kate Lorig et al1 published studies showing the benefits of self-management for various physical conditions in terms of lower costs to the care system and patient satisfaction. The concept is now getting attention in the mental field. In 2009, when the Mental Health Commission of Canada (MHCC) published Toward Recovery and Weil-Being,2 it noted that out of 3 Canadians living with mental illness do get the help they need. Goal 1 of the strategy proposes that people living with mental problems and illnesses are actively engaged and supported in their journey of recovery and well-being, and that people are able to choose programs and treatments to help them achieve the best possible quality of life, health, and social functioning. The MHCC further proposes an active partnership between mental providers, service users, and family members. Goal 5 of the strategy proposes timely access to appropriate and effective treatments which are based on the principle of the least intrusive and least restrictive alternatives. This In Review on self-management aligns with the system goals of the MHCC and offers perspectives on how improved access to self-management for people experiencing mild to moderate depression can be achieved in partnership with patients, families, primary care physicians, and psychiatrists. In their In Review article, Dr Dan Bilsker and colleagues3 point out that annual prevalence rates for depression had increased to 9.5% by 2001 , and it can be assumed that rates may be higher now given the mental effects of the recession. They cite an Alberta study reporting that 3-year prevalence rates are 21.8%. Dr Bilsker et al3 note that most people diagnosed with depression receive treatment in primary care, but primary care is not well equipped to provide optimal care for depressionp m as most people with depression received only a single session of treatment per year and no follow-up. The article reviews supported self-management (SSM), which is a low-intensity behavioural intervention that employs a self-management guide based on cognitivebehavioural therapy principles along with coaching by a care provider, family member, or supporter. has been included in treatment guidelines published by the National Institute for Health and Clinical Excellence (NICE) in the United Kingdom and hence can be seen as highly relevant to the provision of treatment for depression in various settings, especially primary care. Paradoxically, Dr Bilsker et al3 find that the treatment of mild to moderate depression with antidepressants contravenes the NICE guidelines and suggest that the use of SSM has the potential to enhance patient safety while substantially reducing intervention costs.p206 is reviewed using quality assessment criteria, and studies are cited that show the efficacy of the approach. Dr Bilsker et al3 provide a case study that aptly illustrates the clinical utility of SSM. While the approach has potential to improve care in primary care, it also can be applied in other settings. It is presented as a low-cost intervention that can be effectively integrated into collaborative care. In his In Review article, Professor David A Richards,4 from the University of Exeter in the United Kingdom, outlines how stepped care and self-management have been implemented in the United Kingdom. While collaborative care provides an integral platform to build interventions, such as stepped care and SSM, Professor Richards notes that health care delivery systems may be well suited to the implementation of low-burden [on the provider] mental treatments, such as SSM.4. p 2 Mental systems may need to be restructured to support these types of interventions and Professor Richards notes the importance of competency-based training and robust data-outcome measurement systems to drive change. He also notes that telephony-based applications were shown to effectively treat more patients in one study. …
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 | 0,004 | 0,008 |
| 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 | 0,002 | 0,004 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,005 |
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 ».