Film production during the Covid-19 pandemic
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic created unprecedented challenges for the film industry. Following a shutdown of productions, The Safe Way Forward document was developed to outline disease mitigation protocols. Despite this framework, many unanticipated scenarios arose during reopening of film production with the ongoing pandemic. AIMS: To identify and document promising practices for mitigating COVID-19 transmission in the film industry that can inform future pandemics and other industries. METHODS: We conducted a literature search to review research regarding COVID-19 disease mitigation efforts in the film industry. Through client-facing consultancy and consultant group meetings, we identified those factors most important for disease mitigation in the film industry and applicable to future pandemics and other industries. The Delphi Method enabled experts to review lessons learned as studio consultants during the COVID-19 pandemic; learnings were coded and analyzed for recurring themes. RESULTS: We identified anxiety, mistrust, and poor communication as key contributors to decreased compliance with COVID-19 protocols. In response, our team demonstrated multi-specialty expertise, provided scientific explanations, and developed trust by listening empathetically and responding with clear, consistent messaging. These measures served to alleviate anxiety, improve compliance, and provide a safe return to production. CONCLUSIONS: This study demonstrates the ability and agility of multi-disciplinary experts acting in the absence of clear guidance to support a safe return to film production. Workplace anxiety and non-compliance can be alleviated through effective communication by trusted experts. Lessons learned by our consultancy group can help protect workers across diverse industries in future pandemics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.044 | 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".