Undercover Dogs: Pet Dogs in the Sleep Environment of Patients with Chronic Pain
Bibliographic record
Abstract
(1) Background: Chronic pain is a significant and prevalent condition in many industrialized nations. Pain and sleep’s reciprocal nature suggests that interventions to improve sleep may decrease pain symptoms. Little attention has been paid to the influence that owning a pet dog has on the pain/sleep relationship. Typical advice to remove pets from the bedroom negates the possible positive benefit of human-animal co-sleeping. Aim: To investigate pain patients’ perceived impact of pet dog ownership on sleep. (2) Methods: We carried out a content analysis of interview data focused on the impact of pet dog ownership on sleep. The qualitative dataset comes from a subgroup of participants in a larger study examining the pain patient/canine relationship. This subgroup of participants from the larger study was asked, “Does your dog have a positive or negative impact on your sleep?” The data were thematically coded using an iterative approach. (3) Findings: Codes included: companionship; physical presence/’cuddles’; routine/schedule; distraction from anxiety/worry at night; reassuring/protective presence; active intervention to keep participant safe; daytime activity to promote sleeping at night; and reciprocal concern for the sleep of the pet dog. (4) Conclusions: Pet dogs may play important roles in helping people with chronic pain achieve sleep onset and maintenance. Removing the dog to improved sleep could be counter-productive and lead to additional sleep-related issues.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".