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Enregistrement W4411262435 · doi:10.1177/00469580251343779

A Cross-Sectional Survey of Unintentional Injuries Among 15-24-Year-Old Vocational School Youth From Pakistan Between 2021-2022

2025· article· en· W4411262435 sur OpenAlexaff
Sarwat Masud, Adnan A. Hyder, Uzma Khan, Nadeem Ullah Khan, Ahmed Raheem, Pammla Petrucka

Notice bibliographique

RevueINQUIRY The Journal of Health Care Organization Provision and Financing · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueInjury Epidemiology and Prevention
Établissements canadiensUniversity of Saskatchewan
Organismes subventionnairesFogarty International Center
Mots-clésVocational educationCross-sectional studyMedicineFamily medicinePsychologyPedagogyPathology

Résumé

récupéré en direct d'OpenAlex

There is a lack of recent data on the incidence of unintentional injuries and occupational injuries from Pakistan, among youth 15 to 24 years of age. This survey was conducted among vocational school youth in Peshawar, Pakistan (2021-22). Parental consent and assent were obtained for students <18 years of age. After obtaining consent, students were given a hard copy of the self-administered, World Health Organization community survey guide for injuries and violence questionnaire in a classroom session. Incidence Rate Ratios (IRR) were reported for unintentional and occupational injuries There were 547 youth of which [356 (54%)] were males. Majority [535 (97%)] of the students had received formal education before vocational training, while fathers had higher formal education [437(80%)], compared to mothers [326 (60%)]. The median family income of these vocational students was 30 000 Pakistani rupee (PKR) per month. Vocational youth mostly lived in crowded family settings with 239 participants (44%) living with ≥8 family members in the household. In terms of risk behaviors, there was minimal use of tobacco [532 (97.3%)] and minimal alcohol [9 (2%)]. Non-use of helmets was found in [273 (50%)], which was similar to seat belt non-use in [307 (56%)] of participants. Eight percent of students carried a gun for personal protection. Males had 3.24 times higher rates of road traffic injuries, 1.28 times higher rates of occupational injuries, and 1.63 times higher rates of unintentional injuries overall compared to their female counterparts. The 15 to 19 age group had significantly lower incidence of burns and falls compared to the 20 to 24 age group. Factors that increased the risk of unintentional injuries UI T were tobacco use adjusted IRR = 1.25 (95% CI: 1.05-2.69, P = .049), not using a seat belt adjusted IRR = 1.3 (95% CI: 1.14-1.69, P < .001), lack of formal education prior to vocational training in the youth, adjusted IRR of 4.6 (95% CI: 1.12-18.91, P = .034), lack of father’s education adjusted IRR = 4.71 (95% CI: 2.12-10.49, P < .001), lower family income (≤35 000 PKR) adjusted IRR = 2.04 ( 95% CI: 1.04-4.02, P = .039), larger household size (≥8 members), with an adjusted IRR of 3.59 (95% CI: 3.11-5.07, P < .001). In contrast, age ≤19 years showed a higher unadjusted risk (IRR = 2.05, 95% CI: 1-4.2, P = .049), but this association was not significant after adjustment (adjusted IRR = 1.61, 95% CI: 0.8-3.27, P = .184). Marital status and mother’s education were not significantly associated with UI T. This study on vocational youth in Pakistan highlights the critical need for targeted interventions. We recommend prioritizing stricter enforcement of traffic laws, implementing public awareness campaigns specifically for vocational youth, and providing subsidized safety equipment, such as helmets. Furthermore, integrating comprehensive road safety and health education into vocational training curricula is crucial. By addressing these critical areas, significant reduction in injury rates and improved safety and well-being of this vulnerable population may be realized.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,024
Score d'incertitude au seuil0,395

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,033
Tête enseignante GPT0,367
Écart entre enseignants0,334 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2025
Routes d'admission1
Résumé présentoui

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