COVID-19 adaptation and recovery: Human resource and training needs in Whistler, BC
Notice bibliographique
Résumé
This report documents a qualitative research project conducted between May and July 2020 by two researchers from Royal Roads University. The focus of the project was to understand more fully the Human Resource (HR) and training needs of Whistler employers during the early stages of the COVID-19 pandemic. These needs were explored in the following sectors: food and beverage, retail, accommodation, and not-for-profit. The research will assist organizations in these four sectors in Whistler as they adapt and respond to the changing pandemic environment. A literature review explored several major catastrophes with an emphasis on recovery strategies. Ten recovery strategies were identified in the literature, as well as seven lessons learned. Four virtual focus groups were held with representatives from each of the four sectors; these representatives were primarily managers and owners of Whistler-based organizations. A qualitative analysis software program was used to aid in the identification of themes. The resulting themes were further analyzed to develop the findings and recommendations presented in this report. Throughout the discussions with the research participants, there were several consistent findings. The questions and findings are organized into two areas: (1) HR needs, as organizations began to open operations, and (2) professional development and training needs. With respect to HR needs, the following five needs were identified as common issues: staffing, adaptability, uncertainty, communication, and strategies for working in the COVID-19 pandemic. With regard to professional development and training needs, all sectors identified conflict resolution and difficult conversations as priorities. The report lists the training and development needs by sector for managers and owners, and staff and volunteers. The research culminated in the development of the 4C model which focusses on workplace adaptation and recovery. The research will have relevance not only to Whistler, but also to other resort communities that have an economy that is reliant on tourism and hospitality.
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 | 0,004 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,022 | 0,005 |
| Communication savante | 0,005 | 0,002 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».