Notice bibliographique
Résumé
Nearly 23,000 children are hurt doing farmwork in the United States each year, and approximately 100 children are killed, according to the National Agricultural Statistics Service. To combat this problem, the National Children’s Center for Rural and Agricultural Health and Safety in Marshfield, Wisconsin, developed the North American Guidelines for Children’s Agricultural Tasks (NAGCAT) in the late 1990s to help farm parents assess whether their children aged 7–16 are developmentally ready to safely complete various farm tasks. A recent study now offers evidence that NAGCAT has been effective at preventing injuries to children working on farms. The study was led by pediatric scientist Anne Gadomski of the Bassett Research Institute in Cooperstown, New York, and published in the October 2004 American Journal of Public Health. It is the first randomized, controlled trial to test the efficacy of these guidelines in preventing farm injuries. William Pickett, a researcher on agricultural injury at Queens University in Kingston, Ontario, says many interventions are directed at children, which may not be an effective strategy. “You can have the most highly educated, most informed child around,” he says, “but they are not necessarily the ones making the decisions about what they do on the farm and where they are allowed to go.” NAGCAT, on the other hand, provides guidance for those who do make such decisions. The guidelines are conveyed through a professional resource manual and parent booklets. Each booklet covers a set of related farm tasks, such as animal care or tractor work, using a poster format to describe the task, adult responsibilities, potential hazards, and necessary safety precautions. The posters also list developmental abilities a child must possess to perform the task. The study involved 2,454 children on 845 farms in central New York. Some of the farms received NAGCAT information, while others did not. Over 21 months, the researchers collected data on children’s injuries, what they were doing when injured, their general responsibilities, and the number of hours worked. Although the two groups did not differ significantly in overall injury incidence, farms that received NAGCAT information reported fewer injuries related to tasks described in the guidelines. Gadomski says, “We suspect that the average age of the child is going up in terms of being assigned certain tasks now that the parent has a guideline to help them make that assessment. The other issue is [parents now have] some idea of how much supervision certain aged children require in order to do the job safely.” For the most part, children under 7 are not ready to engage in productive agricultural work, says Nancy Esser, an agricultural youth safety specialist at the center. The center therefore recommends that young children not be involved in such work. The study is a welcome addition to the literature in an area where there are few published trials, says Pickett. But NAGCAT addresses only one portion of the pediatric farm injury problem, he says. Similar efforts are needed to address injuries that occur among young children—not necessarily workers—who accompany their worker parents into the farm environment.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,217 | 0,095 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».