Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.032 | 0.020 |
| Insufficient payload (model declined to judge) | 0.245 | 0.100 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".