Behavioral Patterns of Family-Supportive Supervision: A Latent Profile Analysis
Bibliographic record
Abstract
Family-supportive supervision (“FSS”) is viewed by scholars as a best practice in organizations and has been linked to several important individual and organizational outcomes. However, this past research has been fraught with issues that have challenged the utility of these findings. As such, Daniel, Sargent, and Shanock (2023) introduced a new conceptual framework of FSS that suggests supervisors’ enactment of specific helping and hindering behaviors are the critical component of FSS, and they further assert that these behavioral patterns are more important to employees’ evaluations of FSS than any one behavior in isolation. Building on this work, the current investigation explored whether distinct profiles of family-relevant supervisor behavior are experienced by employees, and if so, how these profiles might differentially predict FSS evaluations. Taking a person-centered approach, we used latent profile analysis with data collected in two waves from 257 U.S. workers representing various occupations and industries. Our findings reveal four distinct profiles of supervisor family-relevant behavior patterns (bolstering, obliging, erratic, and impairing) that indeed predict different levels of FSS evaluations. We discuss implications for theory and practice.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".