The role of the human-canine bond in recovery from substance use disorder: A scoping review and narrative synthesis protocol
Bibliographic record
Abstract
Abstract Recovery from substance use disorder (SUD) can be conceptualized as a personal journey that includes connection with self and others, as well as animals – known as the human-animal bond (HAB). Research shows that canines are the most common type of animal integrated into animal-assisted interventions to support people with SUD and that there is growing acknowledgement of companion animals in the lives of people with SUD. Yet, to our knowledge, there has been no review of the evidence related to the role of canines specific to SUD and recovery. To address this gap, the objective of this scoping review is to examine the literature on the role of the human-canine bond with respect to recovery from SUD among adolescents and adults, including how the bond may help or hinder recovery. The review will consider papers that describe the human-canine bond with respect to SUD recovery in any recovery- or therapy-related setting globally. Several databases will be searched for published and unpublished literature in the English language from database inception to present. The Joanna Briggs Institute (JBI) methodology for scoping reviews will be used, and two independent reviewers will screen titles, abstracts, and full texts, and extract information from the included articles using a piloted data extraction sheet. The reference lists of included articles will be examined for any additional sources. A thematic approach will be used to examine the extracted data, and the findings will be presented using a tabular analysis and a narrative summary.
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 | 0.079 | 0.116 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.024 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.073 | 0.008 |
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".