Survey of National Health Service (NHS) orthodontic practitioners in Wales, UK. Part 1: working patterns 2021–2022
Bibliographic record
Abstract
OBJECTIVE: To ascertain the working patterns of the NHS orthodontic workforce in Wales and any possible future changes. DESIGN: Descriptive cross-sectional survey. PARTICIPANTS: NHS orthodontic practitioners in Wales. METHODS: An anonymised email distributed an electronic two-part survey of the Welsh NHS orthodontic workforce. The survey consisted of three sections: (1) demographic information; (2) respondents' working pattern (part 1); and (3) perceptions of professional satisfaction (part 2). RESULTS: Part 1 of the survey yielded a 70.5% response rate (n = 79); 65.8% of the respondents were women. Of the respondents, 45.6% (n = 36) worked full time (F/T), 39.2% (n = 31) worked less than F/T and 15.2% (n = 12) worked more than F/T. Of the male respondents, 81.5% (n = 22) worked 10 sessions or more compared to 50% (n = 26) of women. The respondents undertook 508.5 orthodontic clinical sessions per week within Wales; of these sessions, 87.6% (n = 445.5) delivered NHS orthodontic care. Of the respondents, 8.4% (n = 7) were planning to increase their orthodontic clinical time within the next 2 years, 24.1% (n = 19) were planning to decrease it and 20.3% (n = 16) were unsure. One-quarter of respondents indicated that they were planning to stop clinical orthodontic activity within the next 5 years, including 53.3% (n = 8) of DwSIs, 37% (n = 10) of primary care specialists and 13.3% (n = 2) of consultants. The pandemic was an influencing factor for 80% of these clinicians. CONCLUSIONS: Part 1 of the survey suggested that the majority of the orthodontic workforce was female, were working full time or more, and spent most sessions delivering NHS care. One-quarter of respondents were planning to cease undertaking orthodontic activity within the next 5 years.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".