Identification of Illness Uncertainty in Veterinary Oncology: Implications for Service
Bibliographic record
Abstract
Uncertainty has been identified as the central psychological feature of illness experiences, necessitating a variety of coping strategies to effectively manage it and successfully adapt. Indications of difficulty with adaptation are not in relation to the uncertainty itself, but to how well it is managed. Ongoing research since the 1980s has aimed not only to understand illness uncertainty, but to assess interventions and offer evidence-based recommendations for the provision of service which may alleviate its impact. To the authors’ awareness, similar research in veterinary medicine has not been reported, specifically on clients’ experiences of uncertainty with the illness of their pets. While engaged in a qualitative study with thematic analysis to determine the expectations of clients accessing oncology care services, uncertainty was inadvertently identified as a central theme of the clients’ experience. The study consisted of 43 dog owners participating in 30 independent in-person single and dyadic interviews conducted with standardized open and closed-ended questions from April to October 2009. Thematic analysis (supplemented with content analysis) was performed on transcripts of the interview discussions. Uncertainty, originating from the diagnosis of a serious, life-limiting cancer and its treatment, appeared to have a profound impact on clients’ sense of a just and orderly world; their feelings, thoughts, behaviors, attitudes, and personal expectations in relation to their dog; and their expectations of the service. With uncertainty appraised mostly as a danger, clients appeared to employ multiple coping strategies to reduce uncertainty in the effort to adapt to the new reality of living with and caring for a dog with cancer. The need to manage uncertainty influenced their expectations of the service, specifically for information, ongoing relationships, 24-hour access, and timely care. Our findings have implications for the delivery of specialty oncology services and for client welfare. When working with owners of dogs with life-limiting cancer, results suggest health care providers can facilitate the management of uncertainty to enhance clients’ psychological well-being, thereby supporting clients’ successful adaptation to the cancer experience.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".