Prevention of unintentional childhood injury: A review of study designs in the published literature 2013–2016
Bibliographic record
Abstract
The purpose of this review was to examine the range and quality of published injury prevention research, based on study design. Stratified random selection of journals (based on the average annual number of injury prevention publications) was conducted using a published inventory. Hand searches for empirical research articles on unintentional injury prevention in children and youth (0-19 years) over the four-year period 2013 to 2016, inclusive were conducted. Of the 380 studies identified, the majority were descriptive (133, 35%) or observational (163, 43%), with more than three quarters of the published studies using a "hypothesis-generating" study design. Only 12 (3%) studies were randomized controlled trials, and of the 44 experimental studies, 19 (43%) did not include a comparison group. Transportation injuries predominated, knowledge/attitude/behaviour outcomes were common, and the most common intervention approach was education. The majority of publications were from high-income countries. This review of injury prevention research in children and youth showed that descriptive studies predominate in the published literature, and hypothesis-testing study designs are relatively infrequent. The findings suggest a need for the injury field to support and promote rigorous analytic study designs. In other words, to enhance and strengthen the evidence base for injury prevention policy and practice, injury prevention researchers should consider a greater focus on determination of cause and effect and evaluation of the effectiveness of interventions, particularly engineering and legislative interventions.
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 | 0.049 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.027 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".