Exploring the Effects of Early Life Sexual Abuse in Later Life among Catholic Nuns
Bibliographic record
Abstract
This paper explores the variety of associated responses in later life to early life sexual abuse reported by aging Roman Catholic nuns (heretofore referred to as women religious). Specific attention is given to current effects and the strengths and resources these participants identify when integrating their personal histories of sexual abuse. The influences of their personal spirituality and institutional religious life are explicitly explored as factors in addressing the negative effects upon them in later life, which research participants associated with their sexual abuse. Research on sexual abuse and its effects is extensive, due to a growing awareness and concern about the prevalence of sexual abuse against children and sexual violence against women (Koss et al., 1994). However, investigating sexual abuse rates and its effects among aging populations and specifically Roman Catholic women religious is severely limited. In response to this limitation, Saint Louis University’s School of Medicine conducted a study to advance knowledge about “the consequences of sexual trauma among Catholic nuns in the United States and to compare the child sexual abuse experiences of Sisters with these figures for lay women” (Chibnall, Wolf, Duckro, 1998, p. 4). Twelve participants were recruited from this original study who were sexually abused before the age of 18 and are 65+ years of age.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".