Social Relationships, Well‐Being, and Career Commitment: Exploring Cross‐Domain Effects of Social Relationships
Bibliographic record
Abstract
Cette étude explore dans quelle mesure les relations sociales dans divers domaines de la vie sont liées au bien‐être en général ainsi qu'aux exigences de carrière. Cette étude cherche à savoir si les effets de l'interaction des relations sociales sont nuisibles ou bénéfiques et s'ils mesurent les effets du stress sur un ou plusieurs domaines de la vie. Analysant des données d'enquête recueillies auprès de 1,436 avocats mariés de l'Ouest canadien. Les résultats suggèrent que la spécificité de domaine ne détermine pas forcément la nécessité de documenter les effets des interactions. Les résultats Ils suggèrent aussi que les ressources de soutien ou les activités intégratives ne sont pas forcément toutes bénéfiques et qu'au contraire, elles pourraient nuire au bien‐être ainsi qu'à l'engagement de carrière. This study explores the extent to which social relationships in different life domains are related to overall well‐being and career commitment. This study investigates whether the interaction effects of social relationships are harmful or beneficial, as well as whether they moderate the effects of strain within the same domain or across domains. Survey data were analyzed from 1,436 married lawyers in Western Canada. The results suggest that domain specificity is not necessarily a key to documenting interaction effects. They also suggest that not all support resources or integrative activities are necessarily beneficial and instead may be detrimental to individuals' well‐being and career commitment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".