The use of emergency contraception in Australasian emergency departments
Bibliographic record
Abstract
OBJECTIVE: To review the prescribing of emergency contraception by emergency departments in Australasia and compare it with other providers. METHODS: A postal questionnaire was sent to the director of each of the 79 Australasian College for Emergency Medicine accredited emergency departments in Australasia inquiring about the availability and prescribing habits for emergency contraception within each department. RESULTS: Of the 79 emergency departments, 69 (87.3%) responded to the questionnaire and were aware of the 'emergency contraception regimen'. The majority of departments prescribed appropriately (56%) and only one department did not arrange adequate follow up. Anti-emetics are always used by 45 departments (78.9%). Discussion of future contraceptive needs at the time of presentation was only undertaken by 25 departments (43.9%). Written clinical guidelines for emergency contraception were present in 28 departments (40.6%). CONCLUSIONS: Emergency departments are accessed by patients requesting contraception following unprotected intercourse or contraceptive failure. The prescribing of emergency contraception in Australasian emergency departments is comparable with other providers but substantial improvements could be made. Suggestions to assist this improvement include written clinical guidelines and patient information and purpose-made medication packs.
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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 | 0.011 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".