Retrospective review of a carer-employee workplace intervention
Bibliographic record
Abstract
In response to a growing aging population, carer-employees - who balance both unpaid care and paid employment, have become an increasingly normative phenomenon. In order to support the growing population of carer-employees, some employers have implemented carer-friendly workplace policies aimed at keeping carer-employees employed and healthy. This study sought to retrospectively review the effectiveness of one such carer-employee workplace intervention which had been implemented in a post-secondary institution located in Southern Ontario. The study consisted of a thematic analysis of fourteen semi-structured post-intervention interviews from carer-employee participants. A thematic analysis of the interview transcripts revealed the following themes: (i) the pre-existing circumstances of participants; (ii) recommendations made by participants to improve the intervention; (iii) benefits participants received from the intervention; and (iv) factors limiting the benefits participants received from the intervention. Furthermore, the thematic analysis revealed several positive outcomes commonly experienced by intervention participants, namely: improvements in self-reported mental and physical wellbeing, greater self-confidence in caregiving abilities, and access to respite. These findings align with the larger literature on carer-employees and caregiver-friendly workplace policies.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".