An Evolutionary Concept Analysis of Caring for a Pet as an Everyday Occupatio
Bibliographic record
Abstract
This study explores the everyday occupations of caring for a pet, as conveyed in the North American print media spanning 1999-2008 that discusses pet ownership. The incidence of pet ownership is increasing in North America and research suggests that pet ownership can improve health and well-being. Yet, to date, occupational scientists have contributed little to this growing knowledge base. The present study adopted Rodgers’ (2000) evolutionary concept analysis approach to analyze North American newspapers and bestselling books. Findings were synthesized with historical insights and accounts from around the globe. Analysis revealed that pet ownership is a complex concept consisting of: responsibility, investment, occupational engagement, entrepreneurship, relationships, morality, and attitude. Occupational engagement appeared as the central attribute. The Rubik's Cube emerged as a mental image representing the complexity of pet ownership. Having a mental image to study caring for a pet is the end product of concept analysis and can be useful for occupational scientists studying these occupations in the future.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".