COVID-19 adaptation and recovery: Human resource and training needs in Whistler, BC
Bibliographic record
Abstract
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.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".