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Record W4409169603 · doi:10.2196/67695

Mindfulness-Based Cognitive Therapy for Life (MBCT-L) Versus Stress Reduction Psychoeducation (SRP) for the Improvement of Mental Well-Being in Health Care and Other Public Sector Staff: Protocol for the Well at Work Randomized Controlled Trial

2025· article· en· W4409169603 on OpenAlexvenueno aff
Elena Nixon, Shireen Patel, Priya Patel, James Roe, Neil Nixon, Tim Sweeney, Paul B. Bernard, Clara Strauss, Michael P. Craven, Sam Malins, Rob Goodwin, Laurence Astill Wright, Boliang Guo, Richard Morriss

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsnot available
FundersWellcome Trust
KeywordsPsychoeducationMindfulness-based cognitive therapyMindfulnessMental healthPsychologyCognitive therapyStress managementHealth careProtocol (science)PsychotherapistCognitionMedicineNursingPsychiatryClinical psychologyPsychological interventionAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Mindfulness-based and stress reduction interventions have been recommended by the National Institute for Health and Care Excellence guidelines in England and Wales as effective preventive mental well-being interventions for health care and other public sector staff at risk of poor mental health. OBJECTIVE: This trial aims to assess the effectiveness of the increasingly implemented Mindfulness-Based Cognitive Therapy for Life (MBCT-L) intervention versus a routinely available Stress Reduction Psychoeducation (SRP) intervention in reducing perceived stress and improving other mental health and work-related outcomes in national health care and other public sector service employees. METHODS: The trial is a multisite, single-blind, parallel-group, 2-arm superiority randomized controlled trial. Recruitment, interventions, and assessments will be conducted remotely via online platforms. We will recruit 260 health care and other public sector staff into 26 intervention groups across the United Kingdom, with the intervention delivered through human resource staff well-being provision channels affiliated with participating National Health Service trusts. Participants will be randomly allocated in a 1:1 ratio to either MBCT-L or SRP. Primary and secondary outcomes will be collected at 6, 12, and 20 weeks after randomization. The primary outcome will be the change in scores on the Perceived Stress Scale-14 from baseline to 20 weeks after randomization. Demographic, intervention-related, and health economic data will also be collected. Secondary outcomes will involve assessments of well-being, mental health state, and work-related engagement and performance. Adverse events will be recorded. Data analysis will involve multilevel modeling, and it will be conducted on an intention-to-treat basis. A substudy will involve online semistructured interviews after 20 weeks of randomization with a subsample of participants (n=30, 12%). Transcribed data will be subjected to thematic analysis to elicit qualitative outcomes on perceived well-being and work-related changes after intervention as well as drivers and barriers to intervention uptake and acceptability. RESULTS: Recruitment of participants commenced on August 29, 2023. The target recruitment of 260 participants was reached on April 30, 2024. Follow-up outcome data collection was completed on September 30, 2024, and data analysis is underway. A total of 30 qualitative interviews have been conducted. CONCLUSIONS: Findings will inform future recommendations on intervention suitability and implementation for public care staff well-being. TRIAL REGISTRATION: International Standard Randomised Controlled Trial Number (ISRCTN) ISRCTN18049845; https://www.isrctn.com/ISRCTN18049845. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/67695.

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)0.032
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.064
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.026
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0100.005
Bibliometrics0.0030.004
Science and technology studies0.0040.004
Scholarly communication0.0040.003
Open science0.0040.002
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0640.012

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 head0.150
GPT teacher head0.545
Teacher spread0.395 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreProtocol

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

Citations0
Published2025
Admission routes1
Has abstractyes

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