Challenges of recruiting farm injury study participants through hospital emergency departments
Bibliographic record
Abstract
BACKGROUND: Hospital emergency departments are common recruitment sites for injury studies. Yet recruitment method details, capture rates and response fractions are not consistently reported. As privacy legislation increasingly impinges on research activity, these parameters become even more important. The authors describe their experience with recruitment via emergency departments and outline subsequent adjustments to the recruitment approach. METHODS: The FIRM study was an Australian case-control study of serious farm work-related injury. Cases were identified prospectively by hospital staff on presentation to emergency departments. Consistent with the Victorian Health Records Act, potential cases were initially approached by hospital staff, and full recruitment was subsequently undertaken by study staff. Manual hospital record audits were conducted at five recruitment sites to determine the proportion of eligible cases approached. RESULTS: Among 660 medical records audited, 19 eligible cases were confirmed, 9 of whom were approached by hospital staff (47%, 95% CI 25 to 70%). In response, an additional process was established to capture missed cases, who were sent a letter from the hospital providing the opportunity to opt out of telephone contact by study staff. Early indications were that 34% (41/122) of missed cases actively declined to be contacted. Among those who were contacted and eligible, 84% (21/25) agree to study participation. CONCLUSIONS: Recruitment of injury research participants via hospital emergency departments is challenging, particularly where authorities require an intermediary to make the initial contact. Removal of some constraints imposed by privacy legislation would considerably simplify recruitment and enhance scientific rigour in conducting epidemiological research.
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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 | 0.326 | 0.394 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".