Human-Animal Relational Theory: A Constructivist-Grounded Theory Investigation
Bibliographic record
Abstract
Constructs of human-animal relational theory (HART) were investigated to determine how those constructs manifested in animal-assisted therapy in counseling (AAT-C) from the perspectives of 6 participants (2 counselors, females, ages 28 and 32, both non-Hispanic and White; 2 clients, male and female, ages 55 and 23, respectively, both non-Hispanic and White; and, 2 therapy animals, canines, Labrador retriever and spaniel mix, ages 4 and 5, respectively). Using constructivist-grounded theory, a research team analyzed qualitative data from observations, interviews, and field notes. From the iterative process of multiphasic coding and constant comparison, these findings emerged: (a) consistency between Chandler's (in press) constructs and participants' experiences of AAT-C, (b) more meaningful therapeutic impacts for clients from client-initiated human-animal relational processes (HARPs) than counselor-initiated HARPs, (c) development of rich definitions and descriptions of Chandler's constructs, and (d) descriptions of interactive experiences of AAT-C and client resistance in the context of HART. Clinicians and educators in the field of AAT can apply the processes, practices, and principles from this study in their work to enhance positive therapeutic impacts for clients. As Chandler's constructs were supported in this study, AAT authors and researchers can solve a glaring problem of inconsistent terminology in the AAT literature by using those constructs in future studies and publications as operationalized nomenclature for standardized AAT interventions.
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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 | 0.032 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".