Challenges of Transcultural Caring Among Health Workers in Mashhad-Iran: A Qualitative Study
Bibliographic record
Abstract
<p><strong>BACKGROUND: </strong>One of the consequences of migration is cultural diversity in various communities. This has created challenges for healthcare systems.</p><p><strong>OBJECTIVES: </strong>The aim of this study is to explore the health care staffs’ experience of caring for Immigrants in Mashhad- Iran.</p><p><strong>SETTING:</strong> This study is done in Tollab area (wherein most immigrants live) of Mashhad. Clinics and hospitals that immigrants had more referral were selected.</p><p><strong>PARTICIPANTS:</strong> Data were collected through in-depth interviews with medical and nursing staffs. 15 participants (7 Doctors and 8 Nurses) who worked in the more referred immigrants’ clinics and hospitals were entered to the study.<strong> </strong></p><p><strong>DESIGN: </strong>This is a qualitative study with content analysis approach. Sampling method was purposive. The accuracy and consistency of data were confirmed. Interviews were conducted until no new data were emerged. Data were analyzed by using latent qualitative content analysis.</p><p><strong>RESULTS:</strong> The data analysis consisted of four main categories; (1) communication barrier, (2) irregular follow- up, (3) lack of trust, (4) cultural- personal trait.</p><p><strong>CONCLUSION:</strong> Result revealed that health workers are confronting with some trans- cultural issues in caring of immigrants. Some of these issues are related to immigration status and some related to cultural difference between health workers and immigrants. These issues indicate that there is transcultural care challenges in care of immigrants among health workers. Due to the fact that Iran is the context of various cultures, it is necessary to consider the transcultural care in medical staffs. The study indicates that training and development in the area of cultural competence is necessary.</p>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".