Medical alert dogs are alerting to multiple conditions and multiple people
Bibliographic record
Abstract
Introduction: Emerging reports claim that medical alert dogs (MADs) are alerting to health conditions other than those for which they were trained or first began alerting, and to people other than the person for whom they were trained or first began alerting. The aim of this study was to document this phenomenon empirically and examine whether any variables were associated with dogs alerting to multiple conditions, multiple people, or both. Methodology: MAD owners completed an online survey containing sociodemographic questions about the person to whom the dog alerted, demographic and training questions about the alerting dog, and questions about the conditions to which, and people to whom, the dog alerted. Fisher’s exact tests were used to determine whether there were any significant relationships between the demographic variables and whether or not the dog alerted to multiple conditions, multiple people, or both. Main results/findings: In a sample of MAD owners (N=61), 84% reported that their dog alerted to multiple conditions, 54% reported that their dog alerted to multiple people, and 46% reported that their dog alerted to multiple conditions and multiple people. Analyses revealed that for dogs without formal training for medical alert, there was a marginally significant relationship between the amount of time the primary person had been with their dog before and whether or not the dog alerted to multiple conditions (p = .004, two-sided Fisher’s Exact Test). Principal conclusions and implications for the field: MAD owners commonly report that their dogs alert to multiple conditions and multiple people. This is the first study to empirically document this phenomenon and the findings highlight the need for further studies to investigate the mechanisms by which dogs may be able to detect multiple conditions and/or multiple individuals’ conditions. Factors that may contribute our findings will be discussed.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.012 | 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".