Bibliographic record
Abstract
Uurimistöö eesmärk oli välja selgitada juhtide soo ja vanuse seos vaimset tervist toetavate meetmete rakendamisega Eesti ettevõtetes ajal, mil vaimse tervise probleemid on muutunud aktuaalseks ka töökeskkonnas. Tuginedes 2024. aasta sügisel läbi viidud küsitlusele, uurisin 194 juhi demograafiliste andmete seost ettevõttes rakendatavate vaimset tervist toetavate meetmete arvuga. Statistilise analüüsi viisin läbi Mann-Whitney ja ANOVA testidega. Tulemused näitasid, et nii noorte juhtidega kui ka naisjuhtidega ettevõtetes rakendatakse statistiliselt oluliselt rohkem vaimset tervist toetavaid meetmeid kui vanemate juhtidega ja meesjuhtidega ettevõtetes. Juhtide vanusel ei leitud olulist püsivat seost meetmete rakendamisega. Soo ja vanuse koosmõjul ei leitud statistiliselt olulist seost meetmete arvuga. Juhtide sugu osutus kõige suurema mõjuga teguriks. Tulemused kinnitavad, et juhtide demograafilistel näitajatel on oluline seos organisatsioonide vaimset tervist edendavate algatustega ning sool on suurem seos rakendatavate meetmete arvuga kui vanusel.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.008 |
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".