Evaluation of a Knowledge Mobilization Campaign to Promote Support for Working Caregivers in Canada: Quantitative Evaluation
Notice bibliographique
Résumé
BACKGROUND: As population demographics continue to shift, many employees will also be tasked with providing informal care to a friend or family member. The balance between working and caregiving can greatly strain carer-employees. Caregiver-friendly work environments can help reduce this burden. However, there is little awareness of the benefits of these workplace practices, and they have not been widely adopted in Canada. An awareness-generating campaign with the core message "supporting caregivers at work makes good business sense" was created leading up to Canada's National Caregivers Day on April 5, 2022. OBJECTIVE: Our primary objective is to describe the campaign's reach and engagement, including social media, email, and website activity, and our secondary objective is to compare engagement metrics across social media platforms. METHODS: An awareness-generating campaign was launched on September 22, 2021, with goals to (1) build awareness about the need for caregiver-friendly workplaces and (2) direct employees and employers to relevant resources on a campaign website. Content was primarily delivered through 4 social media platforms (Twitter, LinkedIn, Facebook, and Instagram), and supplemented by direct emails through a campaign partner, and through webinars. Total reach, defined as the number of impressions, and quality of engagement, defined per social media platform as the engagement rate per post, average site duration, and page depth, were captured and compared through site-specific analytics on Facebook, Instagram, Twitter, and LinkedIn throughout the awareness-generating campaign. The number of views, downloads, bounce rate, and time on the page for the website were counted using Google Analytics. Open and click-through rates were measured using email analytics, and webinar registrants and attendees were also tracked. RESULTS: Data were collected from September 22, 2021, to April 12, 2022. During this time, 30 key messages were developed and disseminated through 74 social media tiles. While Facebook posts generated the most extensive reach (137,098 impressions), the quality of the engagement was low (0.561 engagement per post). Twitter resulted in the highest percentage of impressions that resulted in engagement (24%), and those who viewed resources through Twitter spent a substantial amount of time on the page (3 minute 5 second). Website users who visited the website through Instagram spent the most time on the website (5 minute 44 second) and had the greatest page depth (2.20 pages), and the overall reach was low (3783). Recipients' engagement with email content met industry standards. Webinar participation ranged from 57 to 78 attendees. CONCLUSIONS: This knowledge mobilization campaign reached a large audience and generated engagement in content. Twitter is most helpful for this type of knowledge mobilization. Further work is needed to evaluate the characteristics of individuals engaging in this content and to work more closely with employers and employees to move from engagement and awareness to adopt caregiver-friendly workplace practices.
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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 | 0,032 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,007 | 0,003 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».