Multi-stakeholder perspectives of locally commissioned enhanced optometric services
Bibliographic record
Abstract
OBJECTIVES: To explore views of all stakeholders (patients, optometrists, general practitioners (GPs), commissioners and ophthalmologists) regarding the operation of community-based enhanced optometric services. DESIGN: Qualitative study using mixed methods (patient satisfaction surveys, semi-structured telephone interviews and optometrist focus groups). SETTING: A minor eye conditions scheme (MECS) and glaucoma referral refinement scheme (GRRS) provided by accredited community optometrists. PARTICIPANTS: 189 patients, 25 community optometrists, 4 glaucoma specialist hospital optometrists (GRRS), 5 ophthalmologists, 6 GPs (MECS), 4 commissioners. RESULTS: Overall, 99% (GRRS) and 100% (MECS) patients were satisfied with their optometrists' examination. The vast majority rated the following as 'very good'; examination duration, optometrists' listening skills, explanations of tests and management, patient involvement in decision-making, treating the patient with care and concern. 99% of MECS patients would recommend the service. Manchester optometrists were enthusiastic about GRRS, feeling fortunate to practise in a 'pro-optometry' area. No major negatives were reported, although both schemes were limited to patients resident within certain postcode areas, and some inappropriate GP referrals occurred (MECS). Communication with hospitals was praised in GRRS but was variable, depending on hospital (MECS). Training for both schemes was valuable and appropriate but should be ongoing. MECS GPs were very supportive, reporting the scheme would reduce secondary care referral numbers, although some MECS patients were referred back to GPs for medication. Ophthalmologists (MECS and GRRS) expressed very positive views and widely acknowledged that these new care pathways would reduce unnecessary referrals and shorten patient waiting times. Commissioners felt both schemes met or exceeded expectations in terms of quality of care, allowing patients to be seen quicker and more efficiently. CONCLUSIONS: Locally commissioned schemes can be a positive experience for all involved. With appropriate training, clear referral pathways and good communication, community optometrists can offer high-quality services that are highly acceptable to patients, health professionals and commissioners.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".