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Record W4307090896 · doi:10.1111/jpm.12881

Randomized controlled trials of mental health nurse‐delivered interventions: A systematic review

2022· review· en· W4307090896 on OpenAlexaboutno aff
Geoffrey L. Dickens, Mohammed Al Maqbali, Nicole Blay, Nutmeg Hallett, Robin Ion, Louise Lingwood, Mariyana Schoultz, Tracy Tabvuma

Bibliographic record

VenueJournal of Psychiatric and Mental Health Nursing · 2022
Typereview
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthPsychological interventionSystematic reviewMedicineRandomized controlled trialNursingCritical appraisalEvidence-based practiceMEDLINEMultidisciplinary approachHealth careFamily medicineAlternative medicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

WHAT IS KNOWN ON THE SUBJECT?: Well conducted randomized controlled trials provide the highest level of evidence of effectiveness of healthcare interventions, including those delivered by mental health nurses. Trials have been conducted over the years but there has not been a comprehensive review since 2005, and never one including studies conducted outside the UK. WHAT THE PAPER ADDS TO EXISTING KNOWLEDGE?: The paper provides a comprehensive overview of results from randomized controlled trials of mental health nurse-delivered interventions conducted in the UK, Ireland, US, Australia, New Zealand, or Canada and reported 2005 to 2020. It highlights that the trial evidence is limited and offers only partial evidence for interventions that are central to mental health nursing practice. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: Much mental health nursing practice is not supported by the highest level trial evidence. Mental health nurses need to carefully select evidence on which to base their practice both from the mental health nursing literature and beyond. Mental health nurses and other stakeholders should demand greater investment in trials to strengthen the evidence base. ABSTRACT: INTRODUCTION: Nurses are the largest professional disciplinary group working in mental health services and have been involved in numerous trials of nursing-specific and multidisciplinary interventions. Systematic appraisal of relevant research findings is rare. AIM: To review trials from the core Anglosphere (UK, US, Canada, Ireland, Australia, New Zealand) published from 2005 to 2020. METHOD: A systematic review reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses. Comprehensive electronic database searches were conducted. Eligible studies were randomized controlled trials of mental health nurse-delivered interventions conducted in relevant countries. The risk of bias was independently assessed. Synthesis involved integration of descriptive statistics of standardized metrics and study bias. RESULTS: Outcomes from 348 between-group comparisons were extracted from K = 51 studies (N = 11,266 participants), Standardized effect sizes for 68 (39 very small/small, 29 moderate/large) statistically significant outcomes from 30 studies were calculable. All moderate/large effect sizes were at risk of bias. DISCUSSION: Trial evidence of effective mental health nurse-delivered interventions is limited. Many studies produced few or no measurable benefits; none demonstrated improvements related to personal recovery. IMPLICATIONS FOR PRACTICE: Mental health nurses should look beyond gold standard RCT evidence, and to evidence-based interventions that have not been trialled with mental health nurse delivery.

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)0.060
metaresearch head score (Gemma)0.226
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.320

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0600.226
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0200.017
Bibliometrics0.0100.012
Science and technology studies0.0020.002
Scholarly communication0.0050.006
Open science0.0030.003
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.231
GPT teacher head0.599
Teacher spread0.368 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations13
Published2022
Admission routes1
Has abstractyes

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