Brief report: Impact of a horseback riding lesson on youth well-being during the COVID-19 pandemic
Bibliographic record
Abstract
During the COVID-19 pandemic, children and adolescents were at a high risk of experiencing anxiety and stress which undoubtedly had a negative impact on their psychological well-being. While research has shown that canine interactions during this time were beneficial for young people’s well-being, the impact of horse-human activities has not been examined outside of at-risk youth and populations with psychological and/or physical conditions. This pilot study examined whether a recreational horseback riding lesson held during the height of the pandemic improved the well-being of non-at-risk, neurotypical youth. Using the Positive and Negative Affect Schedule for Children (Ebesutani, et al., 2012), the mood and stress of beginner/novice riders (n = 16) at a local stable were evaluated before and after the lesson. The Human-Animal Interaction Scale (Fournier et al., 2016) was also used to assess the quality of the horse-rider interaction to examine its potential impact on the efficacy of riding in improving psychological well-being. Results found a significant increase in positive affect and decrease in negative affect after the lesson compared to before. However, these effects appear to be driven by the group of riders that had a higher quality interaction with their horse. Overall, this study provides initial support for the efficacy of horseback riding for improving well-being in neurotypical, non-at-risk children, and demonstrates that the quality of the interaction between the horse and rider likely plays an important role.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".