The IDEA Framework: Integrating Positive Psychology, Yoga, Hypnotherapy, and Bilateral Stimulation for Safety, Stabilization, and Healing of Well-Being
Bibliographic record
Abstract
This article integrates an empowering lens to showcase a multimodal approach that promotes and develops safety, healing, and well-being with a supportive therapist. It describes and explains tenets of positive psychology, yoga, hypnotherapy, and bilateral stimulation to develop client skills to foster safety and healing and consequently, their well-being. Research has shown that these practices can enhance neural connectivity, improve emotional regulation, and reduce stress responses. As a result, safety and stabilization are established for the client. All four of these modalities integrate empowerment and connectedness for the client. The authors also provide examples of integrating the approaches to support clients and their mental health in specific scenarios. This article is the start of a multimodal or scaffolded approach to empower client healing within mental health. The authors provide an acronym, IDEA, which translates to identify (client issue or presenting problem), determine the first approach, engage in integration, and assess and evaluate progress. The IDEA approach can be utilized with one approach at a time or integrate all approaches as necessary. The client’s well-being is prioritized and their ability to heal is emphasized. This article acts as a foundation for future research regarding pathways to safety and healing for well-being. It also provides an evidence-based structure to assist clinicians and clients with their healing of well-being journeys.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".