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Record W2028226169 · doi:10.1136/ebmh.8.3.60

Rethinking evidence-based practice for children’s mental health

2005· article· en· W2028226169 on OpenAlexafffund
Charlotte Waddell, Rebecca Godderis

Bibliographic record

VenueEvidence-Based Mental Health · 2005
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of British Columbia
FundersUniversity of British ColumbiaMichael Smith Health Research BC
KeywordsMental healthPsychologyEvidence-based practicePsychiatryMedicineAlternative medicine

Abstract

fetched live from OpenAlex

“Efficiency is concerned with doing things right. Effectiveness is doing the right things.” Drucker, 1993 Typically, evidence-based practice (EBP) refers to health practitioners applying the best currently available research evidence in the provision of health services. In other words, EBP challenges practitioners to “do things right” and to “do the right things”. EBP originated in medicine, where an estimated 10 000 new randomised controlled trials (RCTs) are published every year but where an estimated 20%–40% of services still do not reflect the best research evidence.1 Related disciplines such as psychology have also embraced the EBP movement to bridge research and practice in order to improve outcomes for people with mental disorders.2 In children’s mental health, high levels of unmet service need suggest a strong role for EBP. At any given time 14% of children experience mental disorders that cause significant distress and impair their functioning, yet only 25% of these children receive specialised mental health treatment services.3 It is also clear that children’s mental health services often fail to reflect the best available research evidence, leading researchers to argue that EBP is an ethical imperative if we are to improve children’s mental health.4,5 Despite being widely advocated, EBP has nevertheless proved difficult to implement. To some extent implementation barriers are a result of a restricted focus on interventions designed to change simple behaviours performed by individual practitioners, such as prescribing by physicians. These interventions have had only modest effects and need to be integrated with larger organisational and system changes that support EBP.1 However, a greater challenge may be posed by controversies about EBP’s narrow definitions of “evidence,” particularly when applied in mental health.6 Here, we discuss the controversies with regard to implementing EBP in children’s mental health. We illustrate the issues based …

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 imitation

Not 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.

metaresearch head score (Codex)0.473
metaresearch head score (Gemma)0.615
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.473
Threshold uncertainty score0.649

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4730.615
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0070.009
Bibliometrics0.0190.011
Science and technology studies0.0090.060
Scholarly communication0.0360.052
Open science0.0200.036
Research integrity0.0420.094
Insufficient payload (model declined to judge)0.0060.003

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.

Opus teacher head0.540
GPT teacher head0.644
Teacher spread0.104 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2005
Admission routes2
Has abstractyes

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