Should we adjust health education methodology to low-educated employees needs? Findings from Latvia, Poland, Slovenia and Spain
Notice bibliographique
Résumé
OBJECTIVES: The presented study explored health beliefs and experiences as well as health education needs of low-educated employees (LEEs) (incomplete primary, primary, lower secondary and basic vocational education) in comparison to those with higher education (secondary and tertiary education) in four European countries: Latvia, Poland, Slovenia and Spain. The main aim was to identify a specificity of low-educated employees (LEEs) by capturing their opinions, experiences, attitudes and needs concerning health education. MATERIAL AND METHODS: The sample consisted of 1691 individuals with the status of an employee (approximately 400 respondents in each of 4 countries participating in the project). The respondents were aged 25-54 (both the control group and the target group consisted in 1/3 of the following age groups: 25-34, 35-44 and 45-54). The respondents were interviewed during the years 2009 and 2010 with a structured questionnaire concerning their health, health behaviours as well as educational needs concerning health education. RESULTS: The study revealed substantial differences in the attitudes of people from this group concerning methodology of health education. LEEs prefer more competitions and campaigns and less written educational materials in comparison to those with higher education. Additionally, they more often perceive a fee, longer time, necessity to take part in a knowledge test and a concern that their health will be checked as factors that can discourage them from taking part in a health training. On the other hand, LEEs can by encouraged to take part in such a training by a media broadcast concerning the event, snacks or lottery during the training, or financial incentives. CONCLUSIONS: The results of the study proved the need for specific health education guidelines to conduct health education for low-educated employees. These guidelines should take in account the sources of health education preferred by LEEs as well as the factors that can encourage/discourage their participation in trainings concerning health.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».