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Record W4387558546 · doi:10.1080/09638288.2023.2266998

Factors shaping return to work: a qualitative study among heart failure patients in Denmark

2023· article· en· W4387558546 on OpenAlexaboutno aff
Sidsel Marie Bernt Jørgensen, Nina Føns Johnsen, Thomas Maribo, Stig Brøndum, Gunnar Gislason, Maria Kristiansen

Bibliographic record

VenueDisability and Rehabilitation · 2023
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsnot available
FundersNordea-fondenHjerteforeningen
KeywordsQualitative researchHeart failureWork (physics)PsychologyMedicineGerontologyPhysical therapyEngineeringSociologyInternal medicine

Abstract

fetched live from OpenAlex

Purpose For people of working-age diagnosed with heart failure, return to work (RTW) is often a significant rehabilitation goal. To inform vocational rehabilitation strategies, we conducted a qualitative study aiming at exploring patient experienced support needs, and barriers and facilitators to RTW.Materials and methods Ten men and eight women with heart failure (48–60 years) were interviewed in Denmark during 2022. A thematic analysis was conducted using the Sherbrooke model as framework.Results Multiple factors operating at different levels shaped participants’ RTW processes. Personal factors included motivation, mental and physical health, social relations, and financial concerns. Factors in the health care system shaping RTW included access to medical treatment, mental health care, and cardiac rehabilitation. Factors in workplace system shaping RTW included job type, employer support, and social relations. Factors in the legislative and insurance system shaping RTW included authorities’ administration of sickness benefits, professional assistance, vocational counselling, and interdisciplinary cooperation.Conclusion Findings illustrate a need to include vocational rehabilitation within comprehensive cardiac rehabilitation programmes, to identify people in need of support, to improve the coordination of care across the health and social care sectors, and to involve employers, health care professionals, and social workers in individualised RTW strategies.IMPLICATIONS FOR REHABILITATIONVocational re-integration is shaped by multiple factors operating at different levels (including personal factors, work-related factors, factors in the health care system, and factors in the legislative and insurance system).To improve return to work following heart failure, there is a need for multi-level initiatives, including policy measures and efforts to enhance continuity and coordination of care.People with heart failure in need of vocational support should be identified early within comprehensive cardiac rehabilitation programmes.Health care professionals should address work-related issues and provide individualised information and clear advice regarding timely and safe return to work.Individualised return-to-work plans should be developed within interdisciplinary teams across health and social care sectors and involve employers to ensure that they are aware of relevant work accommodations.

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)0.011
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.006
Scholarly communication0.0040.003
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.042
GPT teacher head0.348
Teacher spread0.306 · 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 designQualitative
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

Citations7
Published2023
Admission routes1
Has abstractyes

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