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Enregistrement W4386310507 · doi:10.2196/45624

Perspectives of Patients With Chronic Respiratory Diseases and Medical Professionals on Pulmonary Rehabilitation in Pune, India: Qualitative Analysis

2023· article· en· W4386310507 sur OpenAlexvenueno aff
Rashmi Padhye, Shruti Sahasrabudhe, Mark Orme, Ilaria Pina, Dipali Dhamdhere, Suryakant Borade, Meenakshi Bhakare, Zahira Ahmed, Andy Barton, Mahavir Modi, Dominic Malcolm, Sundeep Salvi, Sally Singh

Notice bibliographique

RevueJMIR Formative Research · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Obstructive Pulmonary Disease (COPD) Research
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute for Health and Care ResearchGovernment of the United Kingdom
Mots-clésMedicineThematic analysisPulmonary rehabilitationQualitative researchRehabilitationReferralPhysical therapyCOPDFamily medicinePsychiatry

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Chronic respiratory diseases (CRDs) contribute significantly to morbidity and mortality worldwide and in India. Access to nonpharmacological options, such as pulmonary rehabilitation (PR), are, however, limited. Given the difference between need and availability, exploring PR, specifically remotely delivered PR, in a resource-poor setting, will help inform future work. OBJECTIVE: This study explored the perceptions, experiences, needs, and challenges of patients with CRDs and the potential of and the need for PR from the perspective of patients as well as medical professionals involved in the referral (doctors) and delivery (physiotherapists) of PR. METHODS: In-depth qualitative semistructured interviews were conducted among 20 individuals diagnosed with CRDs and 9 medical professionals. An inductive thematic analysis approach was used as we sought to identify the meanings shared both within and across the 2 participant groups. RESULTS: The 20 patients considered lifestyle choices (smoking and drinking), a lack of physical activity, mental stress, and heredity as the triggering factors for their CRDs. All of them equated the disease with breathlessness and a lack of physical strength, consulting multiple doctors about their physical symptoms. The most commonly cited treatment choice was an inhaler. Most of them believed that yoga and exercise are good self-management strategies, and some were performing yoga postures and breathing exercises, as advised by friends or family members or learned from a televised program or YouTube videos. None of them identified with the term "pulmonary rehabilitation," but many were aware of the exercise component and its benefits. Despite being naive to smartphone technology or having difficulty in reading, most of them were enthusiastic about enrolling in an application-based remotely delivered digital PR program. The 9 medical professionals were, however, reluctant to depend on a PR program delivered entirely online. They recommended that patients with CRDs be supported by their family to use technology, with some time spent with a medical professional during the program. CONCLUSIONS: Patients with CRDs in India currently manage their disease with nonguided strategies but are eager to improve and would benefit from a guided PR program to feel better. A home-based PR program, with delivery facilitated by digital solutions, would be welcomed by patients and health care professionals involved in their care, as it would reduce the need for travel, specialist equipment, and setup. However, low digital literacy, low resource availability, and a lack of expertise are of concern to health care professionals. For India, including yoga could be a way of making PR "culturally congruent" and more successful. The digital PR intervention should be flexible to individual patient needs and should be complemented with physical sessions and a feedback mechanism for both practitioners as well as patients for better uptake and adherence.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,011
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,038

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,011
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0060,005
Communication savante0,0040,003
Science ouverte0,0010,004
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,027
Tête enseignante GPT0,435
Écart entre enseignants0,407 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations9
Publié2023
Routes d'admission1
Résumé présentoui

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