Pandemic-Related Challenges and Organizational Support Among Personnel in Canada's Defense Establishment
Notice bibliographique
Résumé
In the final week of March 2020, 2.8 million Canadians were away from their usual places of work and engaging in remote and/or telework to mitigate the spread of COVID-19 (Statistics Canada, 2020). The Government of Canada's Department of National Defence (DND) and the Canadian Armed Forces (CAF) were no exception, with most members from the regular force (Reg F), the primary reserve force (P Res), and the DND public service (DND PS) working from home. The COVID-19 Defence Team Survey was administered from April 29th, 2020, and May 22nd, 2020, to gain insight into work, health, and family-related challenges since the onset of the pandemic and change in work arrangements. Responses from five open-ended questions were qualitatively analyzed to determine general themes of concern regarding work, personal, and family related challenges, stress-management and coping strategies, and recommendations for improving the work situation and personal well-being. Given the different roles and conditions of employment, responses of the different groups or "components" of respondents (Reg F, P Res, DND PS) were compared to identify common and unique challenges to inform targeted organizational responses. A total of 26,207 members (Reg F = 13,668, 52.2%; P Res = 5,052, 19.3%; DND PS = 7,487, 28.6%) responded to the survey's five open-ended questions, which yielded a total of 75,000 open-ended responses. When asked about work-related challenges, respondents' most common challenges included dissatisfaction with technology/software, work arrangements, ergonomics, work-life balance, communication within the organization, and the uncertainties regarding career development. In terms of personal and/or family-related challenges, the most common challenges included social isolation, the impact of the pandemic on mental health, school closures and homeschooling, caring for vulnerable family members, and childcare concerns. The most common stress-management and coping strategies included exercise, spending time outdoors, communicating or spending time with family members, household chores/projects, mind-body wellness exercises, and playing games. The most common recommendations made by respondents to improve their work- or personal-related situations included improving technological capabilities, streamlining communication, providing hardware and software necessary to ensure comfortable ergonomics, the provision of flexibility in terms of telework schedules, return-to-work decisions, and the expansion of benefits and access to childcare services. In terms of differences among the components, DND PS personnel were most likely to report dissatisfaction with technological changes and ergonomics, and to recommend improving these technological limitations to maximize productivity. Reg F members, on the other hand, were most likely to recommend increased support and access to childcare, and both Reg F and P Res members were more likely to mention that increased benefits and entitlements in response to the COVID-19 pandemic would be ameliorative. The results of this study highlight several important facts about the impact of the COVID-19 pandemic on personnel working in large, diverse organizations. For example, advancements in organizational technological capabilities were highlighted herein, and these are likely to grow to maintain productivity should remote work come to be used more extensively in the long-term. This study also highlighted the importance of flexibility and accommodation in relation to individual needs - a trend that was already underway but has taken on greater relevance and urgency in light of the pandemic. This is clearly essential to the organization's role in supporting the well-being of personnel and their families. Clear and streamlined communication regarding organizational changes and support services is also essential to minimize uncertainty and to provide useful supports for coping with this and other stressful situations.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,010 | 0,002 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».