Evidence map›Paper›PMID 42152450›Full record

ArticleBMJ open2026

Developing a job retention vocational rehabilitation intervention for people with long covid: a person-based approach.

Clement Boutry, Julie Phillips, Carina Knight, Jain Holmes, Priya Patel, Richard Morriss, Roshan das Nair, Eleanor Douglas, Charlotte E Bolton, Boliang Guo and 1 more

Abstract read
In one paragraph

Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Clement BoutryMental Health and Clinical Neurosciences, University of Nottingham School of Medicine, Nottingham, UK.
Julie PhillipsCentre for Rehabilitation and Ageing Research, University of Nottingham School of Medicine, Nottingham, UK.
Carina KnightRoyal Free Neurological Rehabilitation Centre, Royal Free London NHS Foundation Trust, London, UK.
Jain HolmesCentre for Rehabilitation and Ageing Research, University of Nottingham School of Medicine, Nottingham, UK.
Priya PatelMental Health and Clinical Neurosciences, University of Nottingham School of Medicine, Nottingham, UK.ORCID http://orcid.org/0000-0002-8203-8234
Richard MorrissMental Health and Clinical Neurosciences, University of Nottingham School of Medicine, Nottingham, UK.ORCID http://orcid.org/0000-0003-2910-4121
Roshan das NairMental Health and Clinical Neurosciences, University of Nottingham School of Medicine, Nottingham, UK.ORCID http://orcid.org/0000-0001-8143-7893
Eleanor DouglasCentre for Rehabilitation and Ageing Research, University of Nottingham School of Medicine, Nottingham, UK.
Charlotte E BoltonRespiratory Medicine, Nottingham University Hospitals NHS Trust, Nottingham, UK.ORCID http://orcid.org/0000-0002-9578-2249
Boliang GuoMental Health and Clinical Neurosciences, University of Nottingham School of Medicine, Nottingham, UK.
Kate RadfordCentre for Rehabilitation and Ageing Research, University of Nottingham School of Medicine, Nottingham, UK Kate.Radford@nottingham.ac.uk.ORCID http://orcid.org/0000-0001-6246-3180

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLong covid affects a significant proportion of people following SARS-CoV-2 infection and is associated with persistent symptoms such as fatigue, cognitive dysfunction and breathlessness which can negatively impact a person's ability to return to and remain in work. Although tiered vocational rehabilitation (VR) models have been proposed, these are often generic, lack empirical validation and may not address the complex, fluctuating needs of this population.

objectivesTo co-design a VR intervention (the COVID-19-VR intervention) to support return to work (RTW) for people with long covid (pwLC).

settingPrimary and secondary care.

designMixed-methods target population-centred, person-based approach in three stages: Stage 1: interviews (n=21) with pwLC to identify issues and challenges faced in working with long covid. Stage 2: three co-design workshops with pwLC and service providers to (a) generate guiding principles, (b) identify key intervention features to address work needs, (c) create a logic model to illustrate how the intervention could work and (d) develop a treatment plan and resources. Stage 3: feasibility and acceptability testing in six cases (three critical care admissions, three primary care referrals).

resultsPwLC described work-related problems relating to: fluctuating symptoms (cognition, fatigue and breathlessness), employer, coworker and family's understanding of long covid and workplace adjustments. We developed a 6-session, 12-week individually tailored, remotely delivered intervention that included vocational goal setting, RTW planning, fatigue/symptom management, financial advice, and where permitted, education for family/employers, employer engagement and negotiation of a phased RTW. Following feasibility testing, changes included accommodating the long-term nature of long covid, addressing unmet psychological needs, and adding content on adjustment, processing traumatic experience and performance/symptom anxiety, with extended delivery including monitoring, review and case coordination.

conclusionsPwLC may need specialist help to RTW. Our COVID-19-VR appears feasible and acceptable and warrants further evaluation using a staged approach, prior to any definitive effectiveness trial.

Indexed as

COVID-19Rehabilitation, VocationalReturn to WorkHumansMiddle AgedPandemicsPost-Acute COVID-19 SyndromeSARS-CoV-2COVID-19FatigueFeasibility StudiesPost-Acute COVID-19 SyndromePsychosocial InterventionWorkplace

Identifiers

PMID42152450
PMCPMC13182320

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LicenceCC BY
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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.