Obstacles to the development of workplace health promotion in Poland – the perspective of companies’ representatives
Notice bibliographique
Résumé
BACKGROUND: The aim of the paper is to present the barriers and difficulties faced by companies in Poland in their employee health promotion activities. MATERIAL AND METHODS: The aforementioned obstacles were analyzed in terms of their quantity and quality. Quantitative data come from 5 nationwide surveys conducted in 2000 (N = 755), 2006 (N = 611), 2010 (N = 1002), 2015 (N = 1000) and 2017 (N = 1000), in companies employing ≥50 people. Qualitative data were collected from representatives of such companies by means of an audience survey conducted during a conference (N = 75), 8 focus group interviews (N = 64) and individual in-depth interviews (N = 14). RESULTS: Invariably, the most frequently reported difficulty has been the shortage of financial resources for health promotion, as well as the lack of real support (legal, fiscal) from the government. By 2017, the former was indicated by 53% of companies, and the latter by 48%. A detailed analysis has shown that they are interrelated, and the key barrier is the mentality of employers and managers, i.e., their reluctance to health promotion, the fact that they are convinced of its high costs, a poor knowledge about its importance and implementation methods, and fear of the unknown. Other difficulties include: culture and work organization not fostering health care, a poor preparation and limited opportunities for the staff to implement health promotion, employees' reluctance to employer's health-related activities, some drawbacks of offers addressed to companies by external service providers, and, to a lesser extent, other difficulties arising from the business environment. CONCLUSIONS: To effectively mitigate these barriers, it is necessary to involve the government in the implementation of a coherent strategy to support health promotion in companies. Its main directions would be: shaping employers' awareness, supporting health-related activities through fiscal mechanisms (tax and insurance), educating the managerial staff, and pursuing effective cooperation with external providers of health services for employees. Med Pr. 2020;71(5):569-86.
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 | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 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 ».