‘I fell off and landed badly’
Bibliographic record
Abstract
Forearm fractures are one of the most common injuries sustained by children. Our descriptive study addressed, from the perspective of a child, the following research objectives: (1) to describe their fracture experience and (2) to describe how fractures might be prevented. Photovoice is a unique research strategy by which people create and discuss photographs. This technique has been used to elicit the perspectives of those whose voices are often 'not heard' in research, like children. Participants were recruited from a larger three-year prospective trial and included 10 boys (12.3 ± 1.6 years) and 7 girls (11.3 ± 1.6 years). We asked participants to take pictures to explain where their injury occurred (place), what they were doing at the time (context) and how the fracture had happened (mechanism). We also used semi-structured interview techniques. The following key themes emerged from our interviews: (1) the built environment as a key factor that 'caused' their fracture, (2) the fracture experienced as a journey not an event and (3) strategies to prevent fractures. A simple clinical step to potentially reduce subsequent fractures will be for clinicians to have a brief conversation with their young patients and to listen to the child's personal preventive strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".