Examining competencies for labor and social security attorneys in the field of occupational mental health
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".