FEASIBILITY & COST-IMPLICATION ANALYSIS OF THE CARER-INCLUSIVE ORGANIZATIONAL STANDARD AS AN INTERVENTION
Bibliographic record
Abstract
Canada’s aging demographics has produced a crisis in which the working age population is increasingly required to provide unpaid care for aging friends, family or relatives while maintaining employment responsibilities. Currently, there are 8.1 million carers in Canada, with 6.1 million of these carers simultaneously managing their own careers/paid employment alongside these care duties. This dual role of carer and employee places physical and mental burden on a growing number of carer-employees, and is associated with adverse effects on both health and work performance. The literature suggests that adverse effects such as stress, depression, anxiety are associated with caregiving burden, and can manifest in physical symptoms affecting health such as sleep deprivation or fatigue. Additionally, care-work conflict may impede work responsibilities by increasing absenteeism/presentism, reducing productivity, delaying career development and early retirement from the workforce. As a result of these adverse consequences, an understanding of the role the employer plays in supporting carer-employees needs is critical, as these arrangements are mutually beneficial for both employer and carer-employee. However, evidence of the effectiveness of workplace interventions for carers is nascent; additional investigation is needed in order to bridge this gap and encourage widespread uptake of carer initiatives in the private sector. In this dissertation study, an intervention is implemented within a large-sized workplace. We evaluate the following questions: 1) How has COVID-19 impacted the workplace and the nature of caregiving?; 2) What are the gaps within the workplace pertaining to baseline carer-supportive workplace culture?; 3) Does our designed intervention improve work and health outcomes of employees?; 4) Is the intervention cost-saving from the employer’s perspective? These research questions contribute to the paucity of knowledge on this topic as well as providing actionable evidence and tools for employers and policymakers to stimulate change. We found that with the transition to remote working during COVID, carers were struggling with the work-life balance due to the undefined boundaries between work, care and personal life, this effect was exacerbated by the closure of community carer supports, thereby increasing feelings of isolation. However, flexibility and privacy was gained as a result of this arrangement. In designing a tailored intervention, we highlighted that within our partnered workplace, carers had significantly less coworker support, and employee-rated family supportive supervisor behaviour was dispersed across all potential score ranges. As a result, our designed intervention focused on generating a supportive and approachable work culture for carers, centered around education and consciousness-raising. With the implementation of the intervention however, we found mixed results. We did not observe significant changes in employee health and work outcome variables post-intervention compared to pre-intervention, nor did we find the intervention to be cost-saving. However, carers and managers/HR communicated their positive informative experiences with the intervention and highlighted its capacity of practicality in the future. These findings in conjunction suggests that the intervention may be a starting point for culture change, however, further research is needed across a variety of contexts.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".