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Record 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 on 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

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsnot available
FundersNational Institute for Health and Care ResearchGovernment of the United Kingdom
KeywordsMedicineThematic analysisPulmonary rehabilitationQualitative researchRehabilitationReferralPhysical therapyCOPDFamily medicinePsychiatry

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0060.005
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.027
GPT teacher head0.435
Teacher spread0.407 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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