Examining competencies for labor and social security attorneys in the field of occupational mental health
Notice bibliographique
Résumé
AIM: Labor and social security attorneys (LSSAs) are involved in the field of occupational mental health. However, little attention has been paid to the involvement of LSSAs in this field. This study investigated the occupational mental health competencies that are expected of LSSAs. SUBJECTS AND METHODS: Our investigation utilized the Delphi method. In Step 1, we conducted semi-structured interviews with LSSAs and then created an initial list of competencies based on the interviews and a previous investigation. In Step 2, we recruited LSSAs with 10 or more cases related to occupational mental health. They completed a questionnaire assessing the importance of their work (how important they felt it was to conduct work related to mental health) and level of achievement (how much they felt they had achieved). The respondents were also asked to provide additional competencies (not listed on the questionnaire) if they regarded them as necessary for their work, and these were later added to the list of proposed competencies. In Step 3, we presented the results of Step 2 to the same respondents and asked them to rate their agreement with the proposed competencies. Items with agreement of 80% or higher were set as competencies. We also asked LSSAs about the level of importance of their work and their perceived level of achievement with regard to the additional items created in Step 2. Items for which the level of achievement fell below the median were extracted even if the level of importance of the work fell at or above the median. RESULTS: We recruited 8 LSSAs in Step 1 and created a list of 68 preliminary competencies in 20 fields. We recruited 57 LSSAs in Step 2, and 45 LSSAs completed the survey (response rate: 78.9%). Seven competencies were added to the list as a result. We recruited 34 LSSAs in Step 3 (response rate: 75.6%) . Two items with an agreement rate of less than 80% were removed, resulting in 73 competencies in 20 fields. One of the items with an agreement rate of 100% was "The plan is based on the merits and disadvantages (risks) for both labor and management." CONCLUSIONS: This study identified the competencies required of LSSAs in the field of occupational mental health. Our findings suggest that specifying these competencies will enable efficient training of LSSAs.
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 | 0,014 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».