Impact of Melanoma Diagnosis on Sun-Awareness and Protection: Efficacy of Education Campaigns in a High-Risk Population
Notice bibliographique
Résumé
BACKGROUND: Malignant melanoma is a significant cause of morbidity and mortality worldwide. Sun-awareness campaigns increase public knowledge but may not translate into behavioral changes in practice, which is particularly alarming when reported for individuals in high-risk groups. In particular, patients diagnosed with melanoma are at increased risk of developing subsequent primary melanomas compared with the general population. OBJECTIVES: The study was undertaken (1) to assess whether patients with known risk factors for developing melanoma had been exposed to preventative campaign messages prior to their diagnosis, (2) to quantify whether the diagnosis of melanoma changed sun-related attitudes and behavior, and (3) to assess the adequacy of sun-related advice given to patients with melanoma, as well as their compliance with the advice. METHODS: Using an anonymous questionnaire, 217 patients previously diagnosed with melanoma were interviewed on the source and frequency of received sun-related advice, as well as on their knowledge, attitudes, and behavior toward sun protection before and after the diagnosis. RESULTS: The number of patients who reported receiving sun-related advice after being diagnosed with melanoma increased by 36% (52% pre-vs. 88% postDiagnosis), with advice being given more frequently and more often by a physician (19% pre- vs. 49% postdiagnosis). Furthermore, sun-related attitudes and behavioral practices were positively altered. Yet, patients with known risk factors were not preferentially targeted for advice before their diagnosis. CONCLUSIONS: The diagnosis of melanoma leads to increased sun-awareness and protection. While dermatologists should continue their efforts to promote and reinforce sun-awareness in patients with melanoma, additional emphasis on preventative targeting of high-risk individuals would be of marked benefit in decreasing the overall incidence of melanoma. Non-dermatologists, such as family physicians, can be key players in this preventative campaign, and can be educated to recognize and educate patients at risk, as well as direct them to be followed under dermatology care.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 | 0,000 |
| 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 ».