Pandemic Acceptance and Commitment to Empowerment Response (PACER) Training: Protocol for the Development and Rapid-Response Deployment (Preprint)
Bibliographic record
Abstract
BACKGROUND During a global pandemic, it is critical to rapidly deploy a psychological intervention to support the mental health and resilience of highly affected individuals and communities. OBJECTIVE This is the rationale behind the development and implementation of the Pandemic Acceptance and Commitment to Empowerment Response (PACER) Training, an online, blended, skills building intervention to increase the resilience and well-being of participants while promoting their individual and collective empowerment and capacity building. METHODS Based on acceptance and commitment therapy (ACT) and social justice–based group empowerment psychoeducation (GEP), we developed the Acceptance and Commitment to Empowerment (ACE) model to enhance psychological resilience and collective empowerment. The PACER program consists of 6 online, interactive, self-guided modules complemented by 6 weekly, 90-minute, videoconference, facilitator-led, group sessions. RESULTS As of August 2021, a total of 325 participants had enrolled in the PACER program. Participants include frontline health care providers and Chinese-Canadian community members. CONCLUSIONS The PACER program is an innovative intervention program with the potential for increasing resilience and empowerment while reducing mental distress during the pandemic. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/33495
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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 | 0.029 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.198 | 0.044 |
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".