What to Do When Employees Return to Work after a Perinatal Loss: A Few Best Practices
Bibliographic record
Abstract
The main objective of this exploratory, empirical study of qualitative nature is to shed light on organizational practices that encourage people's return to work after the loss of an unborn child or infant. The literature in human resources does not focus on the specific issue of perinatal loss in a context of returning to work. In fact, to examine the issue, one must turn to studies concerning the return-to-work process after other kinds of personal problems. When returning to work after the loss of an unborn child or infant, parents are often still in the early stages of the grieving process. However, organizations rarely support parents when they return to work. This is why our study targets key organizational practices used when employees experiencing perinatal loss return to work. In order to explore these elements, three discussion groups were held with women who lost their unborn child or infant. Content analysis enabled us to conclude the factor that appears to be more crucial to a successful return to work is the support provided by organizations through various practices, of which the most important are access to an employee assistance program, outside help, and appropriate working arrangements.
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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 | 0.026 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| 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".