Palliative Inpatients’ Experiences with Equine Therapy: A Qualitatively Driven Mixed-Method Exploratory Study
Bibliographic record
Abstract
With advances in modern medicine, Canadians are living longer with chronic illnesses.While many live at home, those in inpatient units may require comfort measures to complement treatment programs.Anecdotal evidence established that equine (horse) therapy can be beneficial, but there has been limited research on using horse therapy within Canada's inpatient palliative care population.The study aimed to understand palliative inpatients' experiences with equine therapy.Of eight adult palliative care unit inpatients recruited by nursing staff, six (aged 58 -82) completed the study.A qualitatively driven mixed-methods research design was used to collect qualitative data via individual interviews with participants and quantitative data through inpatient records and the revised Edmonton Symptom Assessment System (ESASr).The quantitative data, analyzed using a nonparametric sign test, guided the interview questions.Then, narrative analysis of the interview data allowed detailed descriptions and exploration of the participants' real-life experiences.The study's results identified equine therapy as an effective intervention that allowed participants to "live in the moment."Narrative threads of quality of life, fatigue, distraction, reminiscence, and identification with the therapy horse were examined.This research project outlays an innovative approach for conducting horse therapy within an institutional setting.It begins to scientifically address the knowledge gap on the meaning of horse therapy to the adult palliative care inpatient population.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".