Text Messaging and Type 1 Diabetes Management: Qualitative Study Exploring Interactions Among Patients and Health Care Professionals
Bibliographic record
Abstract
BACKGROUND: The diffusion of information and communication technologies (ICTs) in type 1 diabetes (T1D) management has generated a debate on the ways in which ICTs can support the patient-provider relationship. Several studies have focused on text messages. Most of the literature proposes quantitative analysis of the impact of text messaging on the clinical conditions of patients and/or their satisfaction with the technology, while the qualitative studies have focused mainly on patients' perceptions about strengths and weaknesses of this technology. OBJECTIVE: In contrast to past studies, we adopted a qualitative approach for the in-depth examination of patient-health care professionals' interactions in text messaging. METHODS: The study focused on the use of the Trento Cartella Clinica del Cittadino Diabetes System (TreC-DS), a digital platform with a built-in messaging system, in two diabetes centers, integrating message analysis with interviews with patients and health care professionals. Each center focused on a specific patient profile: the first one focused on pregnant women with T1D and the second one focused on adult patients with poorly controlled diabetes. RESULTS: The main results of the study were as follows: (1) Health care professionals and patients perceived the messaging system as useful for sharing information (ie, pregnant women for prescriptions and adults with poorly controlled diabetes for advice); (2) The content and communication styles of the two centers differed: in the case of pregnant women, interactions via text messaging were markedly prescriptive, while in the case of adult patients with poorly controlled diabetes, they were conceived as open dialogues; and (3) Conversations were initiated mainly by professionals; in the cases considered, it was mainly the diabetes center that decided whether a messaging conversation was needed. CONCLUSIONS: The results show how the features of interactions of text messaging changed based on the patient profiles in two different centers. In addition, in both diabetes centers that were involved, the system seems to have laid a foundation for a closer relationship between patients and health care professionals.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".