Contagious precarity: A collective biographical analysis of early-career physiotherapist academics’ experiences of the COVID-19 pandemic
Bibliographic record
Abstract
Background: The COVID-19 global pandemic, and the policies created to respond to it, has had profound and widespread impacts.We -three early career physiotherapist academics aspiring to emancipatory physiotherapy practice -noticed both common and divergent experiences amid the impacts of the initial pandemic response.Aim: To explore the professional contexts in which we operate as physiotherapist academics through an analysis of our COVID-19 pandemic-related experiences.Methods: We used a professional practice analytic framework to systematically explore our individual and collective experiences.The analytic framework consists of three lenses (accountability, ethics, and professional-as-worker), each of which is considered through three questions.Results: The analysis revealed the instability of our working conditions.Among us, there were experiences of the pandemic inducing unmanageable workloads and also experiences of the pandemic providing reprieve.We found that our accountability to departments and funders competed for our professional resources with our ethics of providing quality services.The combination of accountability obligations and ethics commitments often overwhelmed our capacities to sustainably maintain well-being.Caregiver status was an important characteristic determining whether the professional context improved or deteriorated in the early pandemic phase.Conclusion: This analysis can help inform essential changes to professional and academic institutions during and after the COVID-19 pandemic.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| 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 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".