Why surveys are âvery hardâ: exploring challenges and insights for collection of authentic patient experience information with speakers of Australian First Nations languages
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,390 | 0,436 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,003 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,018 | 0,025 |
| Communication savante | 0,020 | 0,019 |
| Science ouverte | 0,005 | 0,017 |
| Intégrité de la recherche | 0,004 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».