Bibliographic record
Abstract
Can J Psychiatry. 2011;56(9):511-513. The preceding articles by Dr Rob Whitley and colleagues' and by Dr Eric A Latimer and colleagues2 review 2 critical aspects of evidence-based mental health (EBMH): incorporating recovery ideology and cultural diversity into research-supported practices. Striving for more effective mental health care will entail addressing these and other challenges. As the US Institute of Medicine3 described the situation, gap between what can and should be and what exists is so large that ... it constitutes a chasm.p 30 In this editorial, we identify 4 other contextual aspects of offering EBMH: practice implementation, decision making, program management, and systems oversight. For each, we describe the issue, the current state of mental health services, and possible future directions. Evidence-Based Practice Implementation Clinicians and program leaders want to support the recovery of their clients by offering practices that consistently promote the outcomes their clients seek. However, even for motivated implementers, the process of establishing a new evidence-based service is complex.4 Important facets of implementation outcome include practice acceptability, appropriateness, fidelity, affordability, and penetration.5 Current approaches to implementation encompass a combination of manuals, fidelity reviews, learning collaboratives, and regional technical assistance centres.6 Technical assistance centres are at the heart of efforts in most regions. Consultants in these centres provide local programs with administrative guidelines and advice, training of new clinicians, ongoing supervision by telephone, and regular visits to monitor progress and suggest further strategies.7,8 This approach has been moderately successful9 and has qualitative research support.7,10,11 However, the science of implementation is very young - the field needs rigorous evidence regarding implementation strategies.12 Emerging strategies for getting evidence-based practices (EBPs) to those who need them emphasize information technology, such as distance learning, telemedicine, computerized self-treatment, and electronic decision supports.13 In theory, electronic systems can incorporate evidence-based guidelines, individual tailoring (now called personalized medicine), and training for clinicians while they are providing services.14,15 Further, electronic records permit direct measurement of aspects of implementation, such as screening and practice penetration (the per cent of people who get a needed screening or service). Evidence-Based Decision Making High-quality health care decisions integrate patient preference, clinical judgment, and the constantly expanding corpus of scientific knowledge.3 Nevertheless, today most decisions rely on clinician judgment alone. Few practice sites routinely ask patients about their preferences.16 The scientific knowledge base is accessible, but using it requires a systematic approach to framing the clinical question, finding the current research, evaluating the quality of the research, deciding if the research applies to the current patient and situation, and using the research in a process of shared decision making.17 Mental health practitioners can master the techniques of evidence-based decision making,'8 but several common barriers reduce their use in everyday practice; for example, time constraints and lack of computer access. The fundamental challenge is to get patient preference, health status (symptoms, function, and side effects), and the relevant scientific evidence to the patient and the clinician in understandable formats at the time when a decision needs to be made.'9 To have direct impact on routine care, the clinician and the patient would need to be able to enter the patient's personal data, specify the question, and access the relevant evidence immediately. This kind of access to patient-relevant, up-to-date science is impossible now because current methods for asking and answering the questions are too complex and time-consuming for most real-world settings. …
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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".