Systematic review of fit note use for workers in the UK
Bibliographic record
Abstract
OBJECTIVES: The fit note, introduced in England, Wales and Scotland in 2010, was designed to change radically the sickness certification process from advising individuals on their inability to work to advising them on what they could do if work could be adapted. Our review aimed to evaluate the following: (1) Is the 'maybe fit' for work option being selected for patients? (2) Are work solutions being recommended? (3) Has the fit note increased return to work? (4) Has the fit note reduced the length of sickness absence? We considered the way in which outcomes vary according to patient demographics including type of health problem. METHODS: Studies were identified by a systematic search. We included all studies of any design conducted in the UK with working age adults, aged 16 or over, from 1 April 2010 to 1 Nov 2017. Risk of bias was assessed using a modified Newcastle-Ottawa Scale. RESULTS: Thirteen papers representing seven studies met inclusion criteria. In the largest study, 'maybe fit' for work was recommended in 6.5% of fit notes delivered by general practitioners (GP; n=361 801) between April 2016 and March 2017. 'Maybe fit' recommendations were made in 8.5%-10% of fit notes received by primary care patients in employment, and in 10%-32% of patients seen by GPs trained in the Diploma in Occupational Medicine. 'Maybe fit' was recommended more for women, those with higher socioeconomic status, and for physical, as opposed to psychiatric disorders. The majority of fit notes with the 'maybe fit' option selected included work solutions. There was inconclusive evidence to suggest that the introduction of the fit note has reduced sickness absence among patients in employment. CONCLUSIONS: Fit notes represent a major shift in public policy. Our review suggests that they have been incompletely researched and not implemented as intended.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".