Impacts of a knowledge mobilization campaign on the uptake of carer-inclusive workplace tools in Canada: A quantitative evaluation (Preprint)
Bibliographic record
Abstract
BACKGROUND Coupled with an aging population and lower fertility rates, there is a growing number of Carer-employees (CEs) – those balancing unpaid care with paid employment. Over 5.2 million Canadians are CEs juggling this dual role, often incurring negative impacts to their mental and physical health as a result. Given that unpaid care makes up 75% of care provided in Canada, the economic importance of supporting CEs extends to sustaining healthcare systems. Supporting and accommodating CEs in the workplace has not only been proved to be beneficial to the wellbeing of CEs, but also the organization around increased productivity and lower turnover rates. Despite the clear advantages of implementing caregiver-friendly workplace practices (CFWPs) in the workplace, many organizations across Canada remain largely unsupportive of CE accommodations. OBJECTIVE The present study evaluated the impact of a knowledge mobilization (KMb) campaign. The primary objective of the campaign was to raise awareness of CFWPs in Canada and increase the uptake of various tools designed to support the implementation of CFWPs. The KMb campaign entailed two phases; Phase I published four articles in national leading industry magazines geared towards the three target audiences: Human Resources Professionals, Occupational Health and Safety Professionals, and Small-Medium sized businesses. Phase II was designed to complement Phase I through a series of three webinars, each built around the content in the published articles. METHODS The present study uses a quantitative methodology using data collected primarily through the various magazine article publishing companies, as well as project partner McMaster Continuing Education. Engagement metrics and analytics associated with each KMb activity were collected through social media platforms and website analytics. Tracking engagement metrics, such as views, unique views, social media impressions, social media clicks, registrations and attendees, were used to evaluate the impact of the campaign. RESULTS The collected engagement metrics and analytics were analyzed to evaluate the campaign activities’ impact on increasing the engagement with and uptake of specific tools. Phase I activities brought in a total of 36,308 views, 2,469 unique views, 55,445 social media impressions and 432 social media clicks across all four articles. The most successful activity was Article 3, pitched towards the small-medium sized business audience. Phase II was successful in attracting the target audiences to further promote and disseminate the tools. Noticeable increases in engagement with the CFWP tools are observed during the months when Article 3 and 4 were published. CONCLUSIONS Results of the campaign suggest that published magazine articles targeted to the respective audiences are the most effective method of knowledge mobilization for this work, recognizing that paid activities had greater reach and better resources for dissemination. Future research in this area should focus on engaging with employers and professional stakeholders more directly.
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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 | 0.025 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".