Learn Not to Burn: A TV Public Service Announcement and Childrenʼs Book for Burn Prevention in Cambodia
Bibliographic record
Abstract
Cambodia is a developing country of 13 million people where there are an estimated 20,000 burn injuries and 2000 burn deaths annually. Two thirds of the burns occur to children under the age of ten. Since 2000, members of a Canadian regional burn centre have been training Khmer surgeons, nurses and therapists at an NGO hospital in Phnom Penh that provides free treatment for disabled people. Burn prevention clearly is a priority and after initial success with a small burn education project in schools of one Cambodian province we were asked to extend the program and offered funding from the Australian and British Embassies. A burn prevention video to be broadcasted on TV was chosen as the most effective medium because Cambodian children watch an average of two hours of television daily (data from school survey). Well recognized burn messages such as “cool the burn with water” and “stop, drop and roll” were selected. Common local burn scenarios were identified by examining hospital records. They included open flame burns, cooking burns, mosquito-net burns and petrol burns. A series of scripts were created and translated into Khmer language appropriate for children age 8–12. Local TV production companies tendered for the contract and the videos were produced entirely using local resources and talent. The total cost of production was US$14,000. The Cambodian Royal Family provided a Royal Blessing for the project and gave a cash donation of US$2000 to cover the cost of broadcasting the messages for the first month.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".