Impacts of a knowledge mobilization campaign on the uptake of carer-inclusive workplace tools in Canada: A quantitative evaluation (Preprint)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,025 | 0,049 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».