Negotiating Tensions Between Theory and Design in the Development of Mailings for People Recovering From Acute Coronary Syndrome
Notice bibliographique
Résumé
BACKGROUND: Taking all recommended secondary prevention cardiac medications and fully participating in a formal cardiac rehabilitation program significantly reduces mortality and morbidity in the year following a heart attack. However, many people who have had a heart attack stop taking some or all of their recommended medications prematurely and many do not complete a formal cardiac rehabilitation program. OBJECTIVE: The objective of our study was to develop a user-centered, theory-based, scalable intervention of printed educational materials to encourage and support people who have had a heart attack to use recommended secondary prevention cardiac treatments. METHODS: Prior to the design process, we conducted theory-based interviews and surveys with patients who had had a heart attack to identify key determinants of secondary prevention behaviors. Our interdisciplinary research team then partnered with a patient advisor and design firm to undertake an iterative, theory-informed, user-centered design process to operationalize techniques to address these determinants. User-centered design requires considering users' needs, goals, strengths, limitations, context, and intuitive processes; designing prototypes adapted to users accordingly; observing how potential users respond to the prototype; and using those data to refine the design. To accomplish these tasks, we conducted user research to develop personas (archetypes of potential users), developed a preliminary prototype using behavior change theory to map behavior change techniques to identified determinants of medication adherence, and conducted 2 design cycles, testing materials via think-aloud and semistructured interviews with a total of 11 users (10 patients who had experienced a heart attack and 1 caregiver). We recruited participants at a single cardiac clinic using purposive sampling informed by our personas. We recorded sessions with users and extracted key themes from transcripts. We held interdisciplinary team discussions to interpret findings in the context of relevant theory-based evidence and iteratively adapted the intervention accordingly. RESULTS: Through our iterative development and testing, we identified 3 key tensions: (1) evidence from theory-based studies versus users' feelings, (2) informative versus persuasive communication, and (3) logistical constraints for the intervention versus users' desires or preferences. We addressed these by (1) identifying root causes for users' feelings and addressing those to better incorporate theory- and evidence-based features, (2) accepting that our intervention was ethically justified in being persuasive, and (3) making changes to the intervention where possible, such as attempting to match imagery in the materials to patients' self-images. CONCLUSIONS: Theory-informed interventions must be operationalized in ways that fit with user needs. Tensions between users' desires or preferences and health care system goals and constraints must be identified and addressed to the greatest extent possible. A cluster randomized controlled trial of the final intervention is currently underway.
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 | 0,164 | 0,182 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,011 |
| Communication savante | 0,010 | 0,008 |
| Science ouverte | 0,005 | 0,006 |
| Intégrité de la recherche | 0,003 | 0,004 |
| 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; 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 ».