Fit Note Use in UK Clinical Practice 2010–2016: A Systematic Review of Quantitative Research
Bibliographic record
Abstract
Background The fit note, introduced in England, Wales and Scotland in 2010, was designed to radically change the sickness certification process from advising on individuals’ inability to work to what they could do if adjustments were made available. Our review aimed to evaluate: (1) the percentage of fit notes utilizing the new “may be fit for work” option or advising on work adjustments, (2) the impact of the fit note on sickness absence and return to work, (3) demographic variation in fit note use. Methods We systematically searched in Embase, Cochrane CENTRAL, Pub Med, Worldcat, Ovid and PsychInfo from 1 Jan 2010–30 Nov 2016 for studies on working aged adults which included the search terms “fit note” or “fitnote”. Relevant abstracts were extracted and we assessed the quality of the papers and assessed bias using the modified Newcastle Ottawa Scale. Results Nine papers met the inclusion criteria, four of which were based on the same cohort. Maybe fit notes made up just 6.6% of all fit notes. Work adjustments were most often recommended for patients who were less deprived, female and patients with physical health problems. Fit note advice for patients with physical health problems increased over time, but the opposite was seen for patients with mental health problems. Conclusions Further research needed to evaluate the use, impact and potential of the fit note, especially for patients with mental illness. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.031 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".