A Scoping Review of Published Research on the Relinquishment of Companion Animals
Bibliographic record
Abstract
Globally, large populations of companion animals are relinquished each year. The purpose of this scoping review was to identify all published research investigating companion-animal relinquishment to map out and evaluate research gaps, needs, and opportunities. A comprehensive search strategy was implemented in 4 online databases, identified citations were screened, and relevant articles were procured and characterized. From 6,848 unique citations identified, 192 were confirmed relevant, including 115 primary-research articles and 77 reviews and commentaries. The majority of these articles originated from the United States (131; 68.2%); 74 (38.5%) of them have been published since 2006. Among the primary-research articles, 84 (73.0%) investigated reasons for companion-animal relinquishment. The most commonly studied reasons were aggressive companion-animal behaviors (49; 58.3%); moving, rental, or housing issues (45; 53.6%); and caretaker personal issues (42; 50.0%). Only 17 primary-research articles investigated interventions to prevent companion-animal relinquishment. The quantity of research into reasons for relinquishment highlights an opportunity for future knowledge-synthesis activities in this area, including systematic review and meta-analysis. In comparison, the limited research into interventions identifies a priority for new research.
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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".