Do physicians and patients speak different languages: quantitative evidence from linguistic analysis
Notice bibliographique
Résumé
Abstract Background Misunderstanding and miscommunication between physicians and patients are well-accepted sources of poor therapy adherence and poor outcomes. Some of these issues are suspected to occur because these two groups speak different "languages". A likely component of such language difference is vocabulary; specifically, the use of medical terminology. Nonetheless, there are other psychological aspects of language such as authoritative and emotional use. The text analysis programme, Linguistic Inquiry Word Count (LIWC), was used to assess such features. LIWC is an established tool that analyses the frequency of words that fall into various linguistic, psychological, and emotional categories. The aim was to compare text from both physicians and patients. The context was the use of anticoagulation therapy in patients with atrial fibrillation. Methods Three articles from English-speaking countries (Australia, Canada, and the UK) were examined. These articles presented transcribed verbal communications between interviewers and groups of physicians and patients. More than 80 separate comments, each ≥ 20 words, made by each group were extracted from the articles and analysed. The total number of words was 8,609 from physicians and 4,804 from patients. Data were collected for the following LIWC analysis categories: (1) the proportion of big words (words with ≥7 letters), (2) clout; high scores suggest that the speaker is confident, authoritative, and has a high level of social dominance. It implies that the individual is speaking from a position of strength or influence; and (3) emotion: this category is integral to understanding how people communicate feelings and emotions. Results As anticipated the number of big words was higher in physician interviews (19±1%) than for patients (12±1%; P=0.0013). The clout indicator also showed the physicians with a higher score (43±6 vs 16±6; P=0.028). In contrast, in the emotion category, patient scores (1.19±0.02) were higher than for physicians (0.58±0.15; P=0.016); primarily because of a three-fold higher negative emotion score (0.53±0.08 vs 0.16±0.11; P=0.048). Conclusions Physicians and patients used language differently. As expected, vocabulary differed; physicians used more big words. This is consistent with greater use of medical terminology, a known source of miscommunication. The higher physician clout score indicates the potential for a power imbalance. The greater use of emotion-related words by patients is consistent with their anxiety and concern. Human judgment and contextual analysis are required to fully understand and address the nuances of communication and associated understanding. Nevertheless, LIWC can play a supportive role. If we identify language patterns likely to result in miscommunication or misunderstanding, steps can be taken to mitigate the effects. LIWC provided quantitative insight into qualitative impressions and revealed subtle, but potentially vital, differences.
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 | 0,111 | 0,408 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,014 | 0,012 |
| Études des sciences et des technologies | 0,002 | 0,006 |
| Communication savante | 0,007 | 0,007 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».