Child sexual abuse survivors: Differential complex multimodal treatment outcomes for pre-COVID and COVID era cohorts
Bibliographic record
Abstract
BACKGROUND: Child sexual abuse (CSA) is a form of early-life trauma that affects youth worldwide. In the midst of the current COVID-19 pandemic, it is imperative to investigate the potential impact of added stress on already vulnerable populations. OBJECTIVE: The aim of this study was to evaluate the effectiveness of a multimodal treatment program on mental health outcomes for youth CSA survivors aged 8-17. Secondary to this, we explored the potential impact of the COVID-19 on treatment outcomes. PARTICIPANTS AND SETTING: Participants of this study were children and youth aged 8-17 who were engaged in a complex multimodal treatment program specifically designed for youth CSA survivors. METHODS: Participants were asked to complete self-report surveys at baseline and at the end of two subsequent treatment rounds. Surveys consisted of measures pertaining to: (1) PTSD, (2) depression, (3) anxiety, (4) quality of life, and (5) self-esteem. RESULTS: Median scores improved for all groups at all timepoints for all five domains. For the pre-Covid participants, the largest improvements in the child program were reported in depression (36.6 %, p = 0.05); in the adolescent program anxiety showed the largest improvement (-35.7 %, p = 0.006). Improvements were generally maintained or increased at the end of round two. In almost every domain, the improvements of the pre-COVID group were greater than those of the COVID-I group. CONCLUSIONS: A complex multimodal treatment program specifically designed for youth CSA survivors has the capacity to improve a number of relevant determinants of mental health and well-being. The COVID-19 pandemic may have retraumatized participants, resulting in treatment resistance.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".