Conceptualizing a Public and Collective Therapeutic Theatre (PACTT): An analysis of Everything is Not Going to Be Ok performance festival
Bibliographic record
Abstract
This article traces the origin and subsequent study of a weekly online theatre festival hosted by two drama therapists during the first seven weeks of the North American COVID-19 lockdown entitled: Everything is Not Going to Be OK (EINGTBOK) . In our process of creating, curating, and hosting this festival, we began to conceptualize new ways of thinking about the role of drama therapy, and therapeutic theatre in particular, in responding to collective experiences of distress and trauma in ways that facilitate individual and collective capacity in the context of public health. In the article that follows, we briefly synthesize the relevant background literature, describe the EINGTBOK festival, and share findings from the survey sent to all performers and audience members following the conclusion of these performances. Findings suggest that the majority of respondents considered EINGTBOK to be fully or partially a form of therapeutic theatre, with the following themes emerging as the most important: processing the pandemic; creation of connection and community; expression and validation of feelings; permission to be human; increased political and international awareness; and aspects of the format as holding and containing. We end with the conceptualization of a theatre and public health intervention called Public and Collective Therapeutic Theatre (PACTT) and offer possible practice and research implications of these findings. • Therapeutic theatre can respond to collective experiences of distress and trauma. • Therapeutic theatre can help foster a sense of connection and community. • Therapeutic theatre can increase political and international awareness. • Therapeutic theatre can help with the expression and validation of emotions. • Public and Collective Therapeutic Theatre (PACTT) is conceptualized as a public health arts intervention.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.039 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".