Notice bibliographique
Résumé
Whether you are a parent, a clinician, a researcher, or some combination of these roles, the past twenty years has provided much to consider in the treatment of children and youth with mental health needs. Not only have we witnessed an explosive growth in knowledge on how to provide care, the complexity of synthesizing and translating this knowledge into best practices has become increasingly apparent. In a health care system where competing agendas for funding and resources exist, the gap between best evidence and the care that is provided is often tempered by patient needs and preferences, health care provider knowledge and behaviours, and system capacity. The pediatric mental health care system has been described as the “orphan’s orphan” in Canadian health care (Kirby & Keon, 2004), a term meant to highlight the chronic under-funding, limited service options, and shortage of trained mental health personnel. Documentation of these issues is not new. It has now been almost 20 years since Dan Offord and colleagues reported that only 1 in 6 children in Ontario receive timely, necessary mental health services (Offord et al., 1987; Offord et al., 1989). The Canadian Mental Health Association continues to emphasize the need for more trained health care providers to adequately meet demand and decrease waiting lists for services. The recently released Leitch Report provides a cautionary warning to Canadians that the prevalence of diagnosable mental disorders in children and youth is increasing and with this increase will come a need for service (Leitch, 2007); the current prevalence rate of 15–20% is expected to increase by 50% by the year 2020 (World Health Organization, 2001). How then to bridge the critical gap between an ever-expanding knowledge base and the resources necessary to adequately address the mental health care needs of children and youth? Knowledge translation science has become a means for studying this gap in health care. While several definitions exist, from a health care perspective, knowledge translation is based, in part, on the understanding that to promote the uptake of evidence, efforts need to be multi-directional and span all levels of the health care system, from patients, families and health care providers to organizational policies and administrators. Further, a collaborative approach that engages key players and recognizes the context (environment) within which care is being provided can maximize benefits to patient care. The authors in this special issue provide innovative and thought-provoking knowledge translation examples from their work. Dr. Evangeline Danseco and colleagues from the Provincial Centre of Excellence for Child and Youth Mental Health in Ontario describe efforts to promote the adaptation, adoption and evaluation of evidence-based practices in mental health organizations. The Centre’s use of evaluation grants for evidence-based programs and community mobilization awards illustrates the influence that modest investments can have on capacity building, partnerships, and health care system innovations. A contribution from the Community Health Systems Resources Group led by Dr. Melanie Barwick describes the role of a community of practice (CoP) in supporting clinicians throughout Ontario to implement standardized outcomes measures for treatment. The CoP approach offers a promising strategy to bridge best practices with diverse geographic and institutional settings, and disciplines. Dr. Patrick McGrath and his research group in Halifax also take on the challenge of geography by providing distance-based treatment to families. Using the Family Help program as an illustrative case, Dr. McGrath’s group document successes and restarts to give us a balanced example of the effort and involvement of multiple stakeholders to improve access and services to families and their children. Emphasizing the interpersonal nature of mental health care, Dr. John Lyons challenges us to regard knowledge translation as a process of communication that values the creation of shared meaning in the information that is communicated between clinicians and patients. Using Total Clinical Outcomes Management as an exemplar, Dr. Lyons describes the need for a formative component to this process that involves the monitoring of outcomes from clinical practices as they happen to provide feedback on the impact of clinical efforts on patient care. In a closing reflection from Dr. Ian Graham and Ms. Jacqueline Tetroe, Canadian leaders in the study and operationalization of knowledge translation, we are provided with a national perspective on the importance of challenging our current practice models, theoretical presuppositions and professional relationships, and asking ourselves, “How can I improve the treatment and care of children and youth with mental health needs?” We hope this special issue strikes a chord with you and challenges you to consider possible knowledge-to-action strategies in your practice. The guest editors and the Journal invite you to participate in ongoing dialogue by either submitting further articles on the theme issue or commentary via letters to the editor.
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 | 0,042 | 0,156 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,007 | 0,008 |
| Communication savante | 0,026 | 0,020 |
| Science ouverte | 0,007 | 0,024 |
| Intégrité de la recherche | 0,032 | 0,020 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,245 | 0,100 |
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 ».