Factors associated with non-participation in one or two follow-up phases in a cohort study of injured adults
Bibliographic record
Abstract
OBJECTIVE: To identify factors associated with non-participation at the 12-month and 24-month follow-up phases of a prospective cohort study of injury outcomes. METHODS: Associations between non-participation at follow-up phases and a range of sociodemographic, injury, health, outcome and administrative factors were examined. RESULTS: An individual's non-participation at 12 months did not necessarily mean non-participation at 24 months. Sociodemographic factors were the most salient for non-participation, regardless of the number of follow-up phases or specific phase considered. CONCLUSIONS: Retention rates in prospective cohort studies of injury outcome may be improved by follow-up of everyone irrespective of previous non-participation, focusing resources to retain men, young adults, indigenous people and those living with people other than family members, and by ensuring that multiple alternative participant contacts are obtained. There is sufficient evidence to be concerned about potential bias given that several of the factors we, and others, have identified as associated with non-participation are also associated with various functional and disability outcomes following injury. This suggests detailed investigations are warranted into the effect non-participation may be having on the estimates for various outcomes.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".