Evaluating employees’ experiences in implementing COVID-19 Safety Protocols in Ontario parks
Bibliographic record
Abstract
The COVID-19 pandemic has created a wide range of public health challenges for park management and staff in Ontario. Green spaces have become a source of resilience during the spread of the virus, partly due to their proven positive impacts on social, mental and physical well-being. With the introduction of social distancing protocols, utilization of Personal Protective Equipment (PPE), and standardizing constant sanitation efforts, park employees are more responsible than ever for the daily implementation of rules governing park safety and security. At the same time, increased visitor numbers and continually changing government protocols that varied between regional jurisdictions have made the work of park employees more challenging. Employees must be the face of parks while dealing with communication discrepancies, overcrowded parks, staff shortages, inconsiderate visitor behaviors, as well as adjusting to COVID-19 protocols and PPE requirements, and protecting themselves. This study analyzes the impacts that COVID-19 had from a park-employee perspective during the months of May through September 2020. The goal is to shed light on how parks can continue to positively adapt to the changing dynamics in a safe manner for both visitors and employees. For this project I interviewed thirteen employees who worked in provincial parks, conservation authority parks and national parks in the summer of 2020, using a standardized questionnaire to collect information on their experience as it pertained to implementing COVID-19 protocols. It can be noted that 84% of participants identified as post-secondary students. Results suggested that multiple challenges had arisen that summer relating to communication barriers, public interaction, and training needs, with experiences varying between the different parks across the province. According to 46% of respondents, the most significant problem that arose in the park during the pandemic was related to interactions with members of the public and/or inconsiderate visitor behavior. The study concludes with recommendations for park management in hopes of contributing to future park-planning initiatives. These recommendations include communication strategies such as incorporating student-staff concerns and ideas through the implementation of an anonymous communication portal; employee training including additional sessions on how to deal with the public during high-stress scenarios; and organization-wide recommendations such as expanding pre-existing ‘learn to camp’ programming to include a COVID-19 focus.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".