Bibliographic record
Abstract
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. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".