The Occupation of Pet Ownership as an Enabler of Community Integration in Serious Mental Illness: A Single Exploratory Case Study
Bibliographic record
Abstract
Community integration through occupational engagement is an integral tenet of occupational therapy. However, little is known about how pets may assist this process. This study explores community integration through pet ownership as a meaningful, lifelong, occupation for one person with bipolar illness receiving Assertive Community Treatment. Using a case study approach, eight qualitative interviews, as well as observation and analysis of pet photos, were conducted with the mental health consumer and also with members of her social network. Data were analyzed inductively according to the constant comparative approach. The findings reveal that pet ownership assisted the person to counterbalance and move beyond stigma through pets as enablers of: “continuity,” “belonging,” “action and self-construction,” “acceptance,” and “participation.” This process was influenced by the “severity of illness,” “view of community,” and “supports and resources.” The results contribute to our understanding of pet ownership as a means to community integration. The study indicates that to enable persons with a mental illness to engage in pet ownership, occupational scientists need to examine and understand this occupation in the broader context of recovery, health, and well-being. A perspective of pet ownership as meaningful occupation challenges occupational therapists to develop strategies to actively engage clients and their pets in their community.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".