The perspectives of clinical level medical students on animal-assisted therapy -A study in Ghana
Bibliographic record
Abstract
Abstract Background The role of animal-assisted therapy (AAT) in complementary and alternative medicine is well acknowledged. AAT is widely patronized, in developed countries such as USA, Canada, and many European countries, but less so in developing countries in Africa including Ghana. For persons in developing African countries and elsewhere to benefit from AAT, healthcare professionals must be acquainted with it to recommend it to their patients when necessary. This study therefore assessed the perspectives of clinical-level medical students on AAT.Method A semi-structured questionnaire was used to collect data in a cross-sectional study from 206 randomly selected clinical-level medical students of the University for Development Studies, Tamale, Ghana. The data was analyzed using Microsoft Excel and SPSS (Version 26) and the results were presented in Tables and charts. The association between demographic variables and the knowledge and attitude of the students were determined using ANOVA, while bivariate Pearson’s correlation was used to measure the relationships between continuous variables. Associations are considered significant when p-value < 0.05.Results The knowledge about AAT among the medical students was very poor (0.971 ± 2.407 over 10; 9.7%); almost all of them (≈ 99.0%) had very little or no exposure to AAT in school or at home. The attitude of the students was however averagely positive (3.845 ± 0.748 over 7; 54.9%), with a perceived health benefit of ATT score of 4.768 ± 1.002 (68.1%). The motivation of the students to acquire more knowledge and skills about AAT mostly through lectures and practical sessions (70.9%) was good (4.809 ± 1.221; 68.7%). Female students were significantly more knowledgeable about AAT than their male counterparts (1.5 versus 0.6; p-value = 0.006). Although no other sociodemographic characteristics had any significant association with knowledge, attitude, and perception of benefit variables, a positive significant relationship existed between them.Conclusion We conclude that the knowledge about AAT among medical students is woefully inadequate and this is worrying given the beneficial complementary role of AAT in achieving SDG 3. Medical schools and healthcare regulators could incorporate alternative medicine in the training and continuous professional development of medical practitioners to improve their knowledge and practice of AAT.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".