Evidence map›Paper›PMID 41495282›Full record

ArticleScientific reports2026

Developing a multidimensional scale of healing tourism experiences based on a mixed methods approach.

Jize Xie, Han Li, Ziyu Chen, Hong Wang, Haiyan Hua

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jize XieChengdu University of Technology, No.1 East 3 road ErXian brige, Chengdu, 610059, PR China.
Han LiChengdu University of Technology, No.1 East 3 road ErXian brige, Chengdu, 610059, PR China. sc_lh@cdut.edu.cn.
Ziyu ChenChengdu University of Technology, No.1 East 3 road ErXian brige, Chengdu, 610059, PR China.
Hong WangChengdu University of Technology, No.1 East 3 road ErXian brige, Chengdu, 610059, PR China.
Haiyan HuaChengdu University of Technology, No.1 East 3 road ErXian brige, Chengdu, 610059, PR China.

Funding

Natural Science Foundation of Sichuan Province 2024NSFSC0270the Ministry of Education's Humanities and Social Sciences Research Youth Fund Project 24YJC890053
6 · The paper itself

Abstract

This paper develops a scale to measure the healing tourism experiences, drawing on grounded theory and extant relevant literature. In Study 1, 30 in-depth interviews with purposive sampling were conducted to establish a conceptual model comprising four dimensions: anticipated healing, embodied healing, interactive healing, and reminiscence healing. Study 2 applied multivariate statistical analyses to refine the scale, and based on 280 valid questionnaires, developed a 13-item instrument representing the four dimensions. Study 3 further tested the validity and applicability of the scale using data from Study 2, confirming its reliability and predictive power. Study 4 was subsequently conducted to validate the model’s predictive utility by testing the relationship between healing tourism experiences and tourists’ willingness to revisit, thereby confirming the criterion-related validity of the scale. The findings contribute both theoretically and practically: the scale offers a robust measurement standard for scholars to investigate the healing tourism experiences in greater depth, and it provides a theoretical reference for practitioners seeking to evaluate and enhance the restorative value of tourism products and services.

Indexed as

Grounded theoryHealing tourism experiencesScale development

Identifiers

PMID41495282
PMCPMC12858923

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.