Women's healthcare consultations on operations: a multidisciplinary provider questionnaire
Bibliographic record
Abstract
BACKGROUND: 30% of UK primary care consultations relate to gynaecology. Servicewomen access healthcare in general more frequently than their NHS counterparts, so military medical professionals are thus more likely to be managing significant numbers of gynaecological conditions on deployed military operations. Little is known about their confidence and preparedness in managing female-specific complaints. This study aimed to assess clinicians' views as to their training and confidence in managing gynaecological conditions; to gauge the need for developing treatment guidelines and specific training opportunities and to establish the frequency and scope of female-specific presentations on a military deployment. METHOD: A retrospective questionnaire-based service evaluation of clinical practice was undertaken via an anonymised questionnaire, which was distributed to 44 randomly selected Afghanistan-based UK military medical professionals in May 2014. All clinicians with sick parade duties were eligible for inclusion. RESULTS: 23 (57.5%) military medical professionals reported managing one or more gynaecological conditions per month while deployed and 4 (25%) doctors treated more than 5 per month. Of those questioned, 21 (52.5%) felt underprepared to manage gynaecological conditions confidently. Two-thirds would attend a short course on the subject, 13 (32.5%) thought gynaecology should be included in medical predeployment training (PDT) and 26 (65%) wanted management guidelines included within Clinical Guidelines for Operations (CGOs). CONCLUSIONS: Military medical professionals treat servicewomen with gynaecological problems on deployment. Half of the medical professionals questioned felt they had insufficient training and experience to do so confidently. Training packages, as part of PDT or stand alone, were reported as acceptable methods of improving confidence and knowledge. The common gynaecological acute presentations were suggested as topics to be included in CGOs.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".