Long-Term Care Worker Experience during the Pandemic: Explorations in Visual Storytelling
Bibliographic record
Abstract
The Coronavirus Disease of 2019 (COVID-19) drastically impacted the nature of work within Ontario's long-term care (LTC) homes as regulatory bodies moved to protect LTC residents through protocols designed to reduce the risk of transmission.This research aims to reveal a view of these impacts from the perspective of employees working in LTC homes.The study utilized a work experience questionnaire and a series of semi-structured interviews to generate both quantitative and qualitative data to pair with existing recommendations within the sector.As a part of the research process, I combined perspectives and expertise from the discipline of journalism with design research practices to explore and reflect on the use of visual storytelling within the field of design.The result of this exploration in storytelling is a prototypical information architecture and visualization that attempts to combine quantitative and qualitative research data in an honest, engaging, and accessible way.Thank you to my co-supervisors-Chantal Trudel from Carleton's School of Industrial Design it has been a privilege to complete this research with your continuous guidance, support, and no small amount of patience.None of this would have been possible without your
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".