Til We Have Voices: A Feminist-Relational Approach to Understanding the Process of Healing and Becoming Whole Through Lifespan Integration Therapy
Bibliographic record
Abstract
This study opened up avenues for exploring the dismembering effects of trauma and the “re-membering” process of healing. Six participants engaged in a 60- to 90-minute semi-structured interview modelled after Elliot’s Change Interview. Utilizing the Listening Guide, the research team identified voices speaking about trauma and recovery. The voices were grouped into three categories: the voices of trauma’s dismembering effects, the voices of turning towards the pain, and the voices of healing. Among the voices of trauma’s dismembering effects were disconnection, dissociation, impasse, and pain. Voices of turning towards the pain included the voices of active acceptance and of mourning. Voices of healing included the voices of personal essence, integration, astonishment, agency, and calm and peace. Examining these voices, we traced patterns of shifting from fragmentation, aloneness, and numbness to wholeness, connection, and presence. This progression highlights the fulfilled potential of personhood through the transformational process of healing in therapy.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.015 | 0.049 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".