Evidence map›Paper›PMID 41624112›Full record

ArticleDepression and anxiety2026

Sustaining Recovery After Low-Intensity Treatment for Anxiety and Depression in NHS Talking Therapies: A Multiphase Participatory and Consensus-Building Study of Stakeholder Priorities and Recommendations.

Saher Nawaz, Penny Bee, Cintia Faija

Abstract read
In one paragraph

Article in Depression and anxiety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Saher NawazSchool of Health Sciences, Division of Nursing, Midwifery and Social Work, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK, manchester.ac.uk.ORCID https://orcid.org/0009-0007-8188-220X
Penny BeeSchool of Health Sciences, Division of Nursing, Midwifery and Social Work, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK, manchester.ac.uk.ORCID https://orcid.org/0000-0002-5600-0400
Cintia FaijaSchool of Health Sciences, Division of Nursing, Midwifery and Social Work, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK, manchester.ac.uk.ORCID https://orcid.org/0000-0002-6497-9196

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Anxiety and depression affect over 500 million people globally. Despite the availability of effective low-cost treatments, like those provided by NHS talking therapies (TT), over half of patients relapse within a year, highlighting the need to co-develop solutions to maintain wellbeing and optimise healthcare. This study used a multiphase participatory design to synthesise evidence on relapse prevention and collaboratively develop evidence-informed strategies for sustained mental health following low-intensity treatment. The three-phase project began by synthesising evidence from diverse sources. Phase 2 involved two patient and two NHS professional/key stakeholder co-design workshops, using the RAND/UCLA appropriateness method. A sustained patient and public involvement (PPI) group reviewed and refined findings, co-designing content for phase 3, which involved a mixed-stakeholder online meeting to finalise key recommendations and priorities. Phase 1 identified 41 evidence-based solutions for rating during phase 2. Across the four phase 2 workshops (

Indexed as

Anxiety DisordersDepressive DisorderPsychotherapySecondary PreventionConsensusConsensus Statements as TopicHumansStakeholder ParticipationState Medicine

Identifiers

PMID41624112
PMCPMC12852061

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.