The Social Practice of Care Hotel Vacations
Bibliographic record
Abstract
Abstract Due to the increase in the number of elderly and people seeking medical care, the hotel market with a blend of care and leisure experiences is expected to grow in the future ( Han, 2013 ; Karuppan & Karuppan, 2010 ; Laesser, 2011 ). The role of care hotels as an intersection between the care and the tourism sectors makes a vacation in a care hotel an interesting social practice to study. In this contribution a social practices approach ( Spaargaren, 1997 ) is applied to investigate how demand and supply interact during a care hotel vacation. Semi-structured interviews are used to identify successful and less successful interactions or practices between senior guests and personnel in five Dutch care hotels. These interactions are related to materials (care and leisure facilities), competences (skills and empathy of the personnel) and meanings (motivations and aspirations of guests) in the care hotel practice (see Shove et al., 2012 ). The results show that a social practice approach combined with a qualitative research method may be more suited to analysing the complex encounters between guests and personnel during care hotel vacations than more traditional theories from service or experience quality studies. Simultaneously, this study makes clear that we need to develop alternative qualitative (and/or quantitative) research methods to study more privacy-related or intimate practices or rituals as in the case of care hotels. VIU logo WLCE logo Information Vancouver Island University World Leisure Centre of Excellence © Bertine Bargeman, Greg Richards and Marleen van Charante-Stoffelen 2018
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".