An Occupational-based Experimental Study of Collective vs. Personal Efficacy to Encourage Sun Protective Behaviors in Head Start Preschool Teachers
Notice bibliographique
Résumé
Background. Skin cancer is one of the most common types of cancers in the United States. An analysis of literature shows that skin cancer can be partially mitigated through personal behavior changes (Glanz, Buller, & Saraiya; 2007, Canadian Centre of Occupational Health and Safety [CCOHS], 2010). Currently national sun-safety educational campaigns promote sun-protective behaviors to individuals who receive exposure due to their work environment (Task Force on Community Preventive Services, 2004). Preschool teachers employed with San Bernardino County Head Start are inadvertently exposed to sunlight during mandatory recess periods posing risks to their health. Purpose. This study evaluated two instructional approaches to skin cancer prevention. The collective efficacy intervention was designed to promote sun-protective behaviors by enhancing teamwork and collaborative work friendships to achieve behavior change. The individual self-efficacy intervention was designed to promote sun protective behaviors by emphasizing personal, rather than collective, responsibility for behavior change. It was theorized that the collective efficacy approach would have a greater impact on knowledge, personal skin cancer attitudes, and lower sun risk-level behaviors than the individual self-efficacy approach. Methods. Teachers from San Bernardino County Head Start were the participants for this educational intervention. Twenty-four preschools were randomized into three groups (n = 8 schools per group); a Collective Efficacy Group (CE), Self-Efficacy Group (SE), or the Delayed Control Group (DC). A prospective multi-center pretest/posttest control group design was used, with data collected at baseline using a self-administered questionnaire. The study group was the independent variable. The dependent variables were population characteristics, participant demographics, sun risk-level (behavior, knowledge, and attitudes. Analysis and Results. Across study groups, the respondents (N = 175) were female (94.9%), married (59%), Hispanic/ Mexican American (53%), Black/African American (20%), and with some college (39%). MANCOVA analysis showed significant improvements for both the collective efficacy and self-efficacy groups in post-test knowledge, attitude, and sun risk-level when compared to the control group (p=.001). Multiple regression analyses assessed whether certain demographics (pretest knowledge, age Hispanic, Black, married, education and Fitzpatric Skin Color affected knowledge, attitude, and sun risk-level. For the three intervention groups the six demographic variables accounted for 36% to 53% of the variance in the regression models for predicting posttest knowledge, attitude, and sun risk-level (behavior). Conclusion. Both intervention types (CE and SE) produced positive results when compared to a delayed intervention control group. Although between-group effects for the two intervention groups were not found, both were superior to the control group indicating that either could be used successfully to teach skin cancer prevention. This study underscores the need for additional studies to determine if between group differences exist and provides initial evidence about using a collective efficacy approach for occupationally-based sun-protective educational interventions.
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 | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».