Why surveys are âvery hardâ: exploring challenges and insights for collection of authentic patient experience information with speakers of Australian First Nations languages
Bibliographic record
Abstract
INTRODUCTION: Health services collect patient experience data to monitor, evaluate and improve services and subsequently health outcomes. Obtaining authentic patient experience information to inform improvements relies on the quality of data collection processes and the responsiveness of these processes to the cultural and linguistic needs of diverse populations. This study explores the challenges and considerations in collecting authentic patient experience information through survey methods with Australians who primarily speak First Nations languages. METHODS: First Nations language experts, interpreters, health staff and researchers with expertise in intercultural communication engaged in an iterative process of critical review of two survey tools using qualitative methods. These included a collaborative process of repeated translation and back translation of survey items and collaborative analysis of video-recorded trial administration of surveys with languages experts (who were also receiving dialysis treatment) and survey administrators. All research activities were audio- or video-recorded, and data from all sources were translated, transcribed and inductively analysed to identify key elements influencing acceptability and relevance of both survey process and items as well as translatability. RESULTS: Serious challenges in achieving equivalence of meaning between English and translated versions of survey items were pervasive. Translatability of original survey items was extensively compromised by the use of metaphors specific to the cultural context within which surveys were developed, English words that are familiar but used with different meaning, English terms with no equivalent in First Nations languages and grammatical discordance between languages. Discordance between survey methods and First Nations cultural protocols and preferences for seeking and sharing information was also important: the lack of opportunity to share the 'full story', discomfort with direct questions and communication protocols that preclude negative or critical responses constrained the authenticity of the information obtained through survey methods. These limitations have serious implications for the quality of information collected and result in frustration and distress for those engaging with the survey. CONCLUSION: Profound implications for the acceptability of a survey tool as well as data quality arise from differences between First Nations cultural and communication contexts and the cultural context within which survey methods have evolved. When data collection processes are not linguistically and culturally congruent there is a risk that patient experience data are inaccurate, miss what is important to First Nations patients and have limited utility for informing relevant healthcare improvement. Engagement of First Nations cultural and language experts is essential in all stages of development, implementation and evaluation of culturally safe and effective approaches to support speakers of First Nations languages to share their experiences of health care and influence change.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".