Bibliographic record
Abstract
Complex trauma is a critical area to explore in terms of the emotional, physical, psychological, social, and spiritual well-being of survivors. This research explored the personal experiences of 12 adult survivors of childhood trauma by interpreting the meanings they attach to their stories of healing. A three phase analysis approach adapted from the work of Lieblich, Tuval-Mashiach, and Zilber (1998) was carried out. Individual interviews were analyzed following a narrative approach to capture each participant’s perspective and meaning. The themes that emerged from the narratives were organized into ten main categories. Five overarching metathemes occurring across all of the participants’ narratives included: Trauma Effects, Establishing Safety, Reclaiming Self, Healing through Relationships, The Healing Journey. The results of this study add important findings that increase understanding of how to address complex trauma in counselling and health care settings in order to restore individual’s sense of safety and well-being. By targeting the possible issues linked to the seven domains of complex trauma that underlie the presentation of traumatized individuals, survivors will feel more supported in their recovery and may be more likely to access appropriate support.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".