The Lived Experience of Human-Pet Relationships among Adult Survivors of Childhood Sexual Abuse: An Interpretative Phenomenological Analysis
Bibliographic record
Abstract
Given the high prevalence and severe consequences of childhood sexual abuse, it is essential to identify ways to support adult survivors. One potential and relatively unexplored resource available to survivors is the human-pet relationship. In the literature, the human-pet relationship is linked to many positive benefits to physiological regulation, mental health, physical health, and social support - areas of functioning where survivors of childhood sexual abuse may be particularly at risk. Despite existing evidence, there is little research on human-pet relationships among survivors of childhood sexual abuse. To help address this gap, this qualitative study explored the lived experience of human-pet relationships among adult survivors of childhood sexual abuse. Utilizing data collection and analysis methods from Interpretative Phenomenological Analysis, semi-structured interviews were conducted with 10 adult survivors of childhood sexual abuse. The following themes were developed from the data: (a) close bond with pet; (b) idiosyncrasies within the human-pet relationship; (c) moral responsibility; (d) fundamental differences between pets and humans; (e) safety in the human-pet relationship; (f) resource for coping with painful experience; (g) positive impact on well-being; (h) buttress for human-human social interaction; (i) medium for skill and knowledge development; and (j) shortcomings of the human-pet relationship. Findings are discussed in the context of the existing literature, along with considerations for practice and future research with childhood sexual abuse survivors.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".