LESSON LEARNT IN INJURY CONTROL: AFTER 30 YEARS WHAT WOULD I DO DIFFERENTLY?
Bibliographic record
Abstract
Our current view of injury prevention is shallow, narrow and unfocused. We may need to re-examine our approach to injury control to include a more holistic approach that is essentially based on the social determinants of health. Injuries do not happen in isolation. Like the layers of an onion, it is a combination of interplay between various pathophysiologic pathways; constitutional factors; individual risk factors and choices; social relationships; living conditions; neighbourhoods and communities; institutions and finally social and economic policies that contribute to injury causation. From the moment we are conceived and especially up to the age of 18 months our nurturing impacts us for the rest of our lives. We have a choice to invest the time and resources when the infant's brain is most malleable or by default continue to spend too late and at a greater cost in social services, welfare, judicial systems and health care expenditures. Without a doubt, our chances of achieving greater health are directly linked to the strengthening of the social determinants of health: genetics, gender, housing, education (literacy), income and social status, personal health practices, resilience, nutrition, employment conditions, physical environment, culture, child development, spirituality and strong social support networks. Our inability to acknowledge the importance of ‘love’ and ‘happiness’ in our lives makes the required new thinking of injuries difficult for the majority of injury prevention leaders. The current desire for instant versus delayed gratification in our society directly leads to the need for an expensive, ineffective health care system. The very actions that give usinstant gratification also give rise to disease and injury.Fatty foodsgive rise to obesity, diabetes, heart disease, and injury.Drug usegives rise to overdoses, addictions, mental illness, crime and injuries.Alcohol usegives rise to cirrhosis, mental illness, violence, injuries and crime.Inactivitygives rise to obesity, poor health, and disability.Salt usegives rise to hypertension and cardiovascular problems.Sexgives rise to sexually transmitted diseases, unwanted pregnancies, fetal alcohol syndrome and sexual assaults.Smokinggives rise to cancer, chronic obstructive pulmonary disease, bronchitis and injuries. It is estimated that 50% of all deaths in developed countries are preventable. That for those under the age of 44, injury is the leading cause of death. For those under the age of 34 motor vehicle related injuries are the leading cause of death. And finally, injuries kill more 1–19-year-olds than all other diseases combined. Let us broaden our approach to injury prevention by boldly linking our discussions to include ‘love’, ‘happiness’, ‘instant versus delayed gratification’ and the ‘social determinants of health’ as we search for solutions to this seemingly elusive problem—injuries.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.016 | 0.039 |
| Insufficient payload (model declined to judge) | 0.019 | 0.011 |
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".