The Development and Validation of the Workplace Emotion Validation Scale
Bibliographic record
Abstract
The prevailing understanding of workplace emotional support strategies is rooted in Interpersonal Emotion Regulation Theory, which posits that we regulate others’ emotions in the same way as we regulate our own. Drawing on this theory, the current literature has identified two ways in which emotional support is delivered at work: cognitive change and attention deployment. However, this framework assumes that what works at the intrapersonal level (i.e., self-emotion regulation) will be equally effective in an interpersonal context (i.e., support provision), overlooking the unique aspects of social interactions that can contribute to one’s well-being. To address this gap, we conceptualized a new strategy: workplace emotion validation—the affirmation of a support seeker’s emotions from a negative event at work. Grounded in Shared Reality Theory, we proposed that emotion validation alleviates distress by satisfying belongingness and epistemic needs during social interactions. Using 8 samples (total N = 1751), which included multi-wave and multi-source data, we developed a scale to measure emotion validation that considered two support sources—colleagues and supervisors. The scale possesses good construct validity, incremental validity, reliability and psychometric properties. Relationships with various other organizational constructs were also demonstrated. This research challenges the current conceptualization of workplace emotional support strategies and, at a broader level, suggests that emotional support should be examined through specific strategies. Practically, the new scale offers actionable guidelines to support colleagues and subordinates.
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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 | 0.022 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".