Evaluating a Mindfulness Training Program for Chinese Tour Guides
Bibliographic record
Abstract
There is growing pressure on tour guides to encourage responsible and sustainable behaviors amongst their tour participants focusing attention on tour guide training. Despite calls for the inclusion of concepts such as mindfulness and sustainability in tour guide training there has been limited academic discussion of how these might be applied in practice in the Chinese context. This article explores the impact of an online socio-cognitive mindfulness training program on Chinese tour guides’ understanding of sustainability and their perceptions of how to communicate this to their guests. Twenty-seven Chinese tour guides participated in the training and completed semi structured pre and post training surveys. Prior to training, most reported little understanding of sustainability in tourism. Afterward, guides expressed greater recognition of their role in managing tourist impacts and reported intentions to adopt more multisensory, personalized interpretation and storytelling to foster mindfulness among participants. Findings suggest that while guides’ views of mindfulness aligned with Western frameworks, they adapted its use to Chinese guiding practices. Overall, the training enhanced awareness of sustainability and supports incorporating socio-cognitive mindfulness into tour guide education. Thess results support the value of incorporating socio-cognitive mindfulness into tour guide training to enhance awareness of tourism sustainability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".