Becoming More Rugged and Better Resourced: The R2 Resilience Program’s© Psychosocial Approach to Thriving
Bibliographic record
Abstract
The past decade has seen growing interest in interventions that build resilience as a complementary practice to trauma-informed care. From school-based programs focused on self-regulation and academic success to programs that support the well-being of disadvantaged populations or healthcare workers at risk of burnout, the concept of resilience is being used most commonly for programming that builds the capacity of individuals to adapt under conditions of adversity. Critiques have raised concerns that resilience-promoting programs demonstrate bias toward changing individual-level factors such as cognitions (e.g., mindfulness and grit), behavior (e.g., expressing gratitude and changing personal routines), or attachments (e.g., feeling secure in relationships) which help people adapt to socially toxic situations without changing access to the resources they require to overcome exposure to adverse psychosocial factors. This trend belies advances to the theory of resilience which support a more contextualized, multisystemic understanding of how external protective factors (resources) enhance individual qualities (ruggedness) and vice versa. Building on a multisystemic description of resilience, the R2 Resilience Program© was developed and piloted with six different populations ranging from clients of urban social services to workers in a long-term care facility, managers in the health care sector, staff of a Fortune 500 corporation, students in a primary to grade 12 school, and adult volunteers affiliated with an international NGO. Focused on building both individual ruggedness and enhancing people's resources (the two Rs), the program provides contextualized content for each population by selecting from 52 resilience promoting factors with a strong evidence base to create training curricula that enhance the personal qualities and social, physical, and institutional resources most likely to support resilience. This paper reviews the justification for a multisystemic approach to designing resilience interventions and then explains the process of implementation of the R2 program. Preliminary findings are reported, which suggest the program is experienced as effective, with evaluations ongoing.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".