Exploring Work and Non-Work Recovery: Dynamics Across Individuals, Couples, and Contexts
Bibliographic record
Abstract
In alignment with the theme for AOM 2023, our symposium seeks to put the individual worker at the forefront and focus on how we can help essential workers recover from workplace challenges that affect them both at work and outside of work. As workers take part in intense work intervals, it necessitates a recovery period in which they can relax and recover from workplace strains and demands (Sonnentag, Mojza, Demerouti, & Bakker, 2012). Researchers have proposed four different experiences of recovery (psychological detachment, mastery, control, and relaxation; Sonnentag & Fritz, 2007), and empirical work has begun to examine how workers use these processes individually and in tandem (Bennett, Gabriel, Calderwood, Dahling, & Trougakos, 2016; Sonnentag, Binnewies, & Mojza, 2008). The prevailing assumption is that when workers recover, it not only benefits the worker’s level of strain from workplace stress but also prompts other positive outcomes for the worker. Yet, our understanding of recovery experiences has been relatively limited in terms of outcomes. While research has connected recovery processes to outcomes such as job performance (Liu, Ji, & Dust, 2021) and job engagement (Sonnentag, 2003), there remain a great deal of personal and professional outcomes that our papers seek to connect directly to recovery processes. Our symposium investigates the ways that recovery practices can enhance personal and workplace outcomes by examining both common and novel recovery processes (including sleep, leisure time, social support, and coping) and considering their impacts on individual work behavioral outcomes. Specifically, the papers in our symposium explore behavioral outcomes of physiological and psychological resource replenishment, career outcomes from dyadic leisure practices, employee voice outcomes from discussing work at home, and well-being outcomes from collections of individual coping strategies. Across these papers, we also take a dynamic approach to consider how recovery processes and subsequent effects can differ across time. Understanding Parallel & Synchronized Leisure Practices for Couples & Influence on Career Outcomes Author: Katelyn Zipay; Purdue U. Author: Catherine Kleshinski; Indiana U., Bloomington Author: Savannah Conder; Indiana U. “How Was Work Today?” An Enrichment Model of Spouse Voice Cultivation to Propel Voice at Work Author: Nathan Black; U. of Iowa Author: Daniel Newton; U. of Iowa Author: Amy Bartels; U. of Nebraska, Lincoln Author: Brandon Mathew Fogel; U. of Nebraska, Lincoln Author: Stephen Hyrum Courtright; Tippie College of Business, U. of Iowa Latent Transitions of Coping with Work-Nonwork Stressors Author: Catherine Kleshinski; Indiana U., Bloomington Author: Kelly Schwind Wilson; Purdue U., West Lafayette Author: Julia Stevenson-Street; Purdue U., West Lafayette Author: Lindsay Mechem Rosokha; Purdue U., West Lafayette
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".