Evaluating the Uptake of the Canadian Standards Association (CSA) B701:17 (R2021) Carer-Inclusive and Accommodating Organizations Standard Across Canada
Bibliographic record
Abstract
In Canada, 67% of unpaid caregivers are simultaneously balancing paid employment with unpaid care, equating to over 5.2 million Canadian Carer-Employees (CEs). This balancing act often incurs negative impacts on CEs’ health and well-being, including burnout, resulting in adverse effects on their labour force participation. To mitigate these social and economic impacts, McMaster University partnered with the Canadian Standards Association (CSA) to develop the CSA B701:17 (R2021) Carer-inclusive and accommodating organizations standard and accompanying handbook B701-18HB Helping worker-carers in your organization. Since publication in 2017, there has been minimal uptake of the Standard across Canadian workplaces, with just 1062 complimentary downloads total. To determine the level of uptake across workplaces in Canada, the present mixed-methods study used purposive sampling to collect survey (n = 71) and semi-structured interview data (n = 11). The survey data was analyzed for descriptive statistics and logistic regression modelling. The interview data were thematically analyzed for common CFWPs and barriers to Standard uptake. It was found that only 24% of workplaces have implemented the Standard into their workplace practices, with full implementation and current supports as strong predictors of formal uptake. Prominent themes around barriers to uptake and existing organizational policies highlight the critical importance of workplace culture in facilitating CFWPs.
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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 | 0.037 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".