ArticleFrontiers in veterinary science2026
A non-separation diagnostic framework for assessing canine attachment structure.
Article in Frontiers in veterinary science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Conventional diagnostic approaches to canine attachment and separation-related behaviors rely primarily on behavioral outcomes observed during induced separation from the caregiver. While widely adopted, separation-based assessments may conflate superficially similar behaviors that arise from fundamentally different caregiver-dog relational structures (e.g., secure regulation vs. avoidant disengagement vs. ambivalent hyper-proximity), thereby raising ethical, conceptual, and interpretive limitations. This study proposes a non-separation diagnostic framework for assessing canine attachment structure through everyday interactional observation, without inducing caregiver absence. Drawing on long-term naturalistic observation of dogs living in stable human environments and structured non-separation observation of representative cases, attachment-related organization was consistently observed as identifiable prior to any separation event. Diagnostic assessment focused on exploration-return organization (i.e., exploratory expansion followed by repeated voluntary returns to caregiver proximity as a regulatory anchor), distance regulation patterns, emotional stabilization following return, and caregiver emotional responsiveness. Across observed cases, attachment structures were consistently observable under non-separation conditions. Behaviors later expressed during separation were found to reflect amplification of pre-existing relational organization rather than responses generated by separation itself. Two procedural cases are presented to illustrate the diagnostic necessity of this framework. In the first, a presentation superficially resembling secure attachment was procedurally excluded due to absence of exploration and failure of emotional regulation. In the second, overt separation-related distress was observed, yet secure attachment organization was supported through intact non-separation structure. Together, these cases demonstrate that neither proximity nor separation behavior alone can serve as diagnostic criteria. The proposed framework repositions separation as an expressive context rather than a causal diagnostic trigger and establishes non-separation observation as the primary context for attachment classification. This approach offers a conceptually coherent and ethically grounded diagnostic alternative and provides a methodological foundation for subsequent applied and clinical studies of canine attachment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.