The timing and type of nursing staff occupational injury and illness incidents, Veterans Health Administration, 2002-2011: a retrospective, population-based, descriptive analysis
Notice bibliographique
Résumé
Background: While the majority of occupational injuries and illnesses that result in lost work days occur during typical day shift hours, the U.S. Bureau of Labor Statistics has noted that timing patterns often reflect the unique nature of different occupations. However, a literature search indicated that few studies have assessed the interplay between the timing and the type of nursing staff occupational injury and illness incidents in general, but especially so for those incidents that were recorded on an hourly basis. Methods : This decade-long retrospective population-based study ascertained the timing of diverse types of reported occupational injury or illness incidents among Veterans Health Administration (VHA) nursing employees, who were classified between the nurse, practical nurse, and nursing assistant series. Using January 1, 2002 as the start date for the longitudinal surveillance of incidents, descriptive analyses included 55,424 VHA nursing employees who reported a total of 113,708 incidents between 2002 and 2011, of which 106,216 (93.4%) were retained for this study, because they included both the specific time and the specific type of incident involved. Although nursing staff work shifts can vary widely, three “typical” 8-hour work shifts–that is to say, night shift: 23:01-07:00, day shift: 07:01-15:00, and evening shift: 15:01-23:00–were selected for summarizing study findings (i.e., for incidents that included both the specific time and the specific type of incident involved). Results: Findings indicated that male nursing staff (accounting for 15.4% of the applicable occupational injury and illness incidents) reported a larger percentage of “Assaults” and “Lifting (Patient Care)” incidents, especially during the evening and the night shifts, whereas female nursing staff (accounting for 84.6% of the applicable incidents) reported a larger percentage of “Slips, Trips, and Falls” incidents, but these were more likely to occur during the beginning and the end of each shift. Findings also indicated that, regardless of gender, “Assaults” and “Lifting (Patient Care)” incidents were more commonly reported during the evening and the night shifts, as compared with the day shift, between all three nursing occupations (nurse, practical nurse, and nursing assistant). “Slips, Trips, and Falls” incidents were more commonly reported during the beginning and end of each shift, between all three nursing occupations. Conclusions: Staffing patterns and nursing staff working conditions are risk factors for occupational injuries and illnesses. Findings suggest that more attention is needed for ascertaining the potential role and functioning of targeted injury prevention training initiatives with respect to the timing and potential likelihood of selected types of nursing staff occupational injury or illness incidents.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
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 ».