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Record W3152833108 · doi:10.2196/28728

COVID-19 Health Crisis and Chronic Illness: Protocol for a Qualitative Study

2021· article· en· W3152833108 on OpenAlexvenueno aff
Élise Ricadat, Aude Béliard, Marie Citrini, Yann Craus, Céline Gabarro, Marie‐France Mamzer, Ana Marques, Thomas Sannié, Maria Teixeira, Marko Tocilovac, Livia Velpry, François Villa, Louise Virole, Céline Lefève

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
FundersFondation de FranceAgence Nationale de la Recherche
KeywordsPsychosocialHealth careMedicineQualitative researchPandemicNursingPsychologyDiseaseFamily medicinePsychiatryCoronavirus disease 2019 (COVID-19)SociologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: The acute nature of the COVID-19 pandemic has put a strain on health resources that are usually dedicated to chronic illnesses. Resulting changes in care practices and networks have had major repercussions on the experience of people with chronic disorders. OBJECTIVE: This paper presents the protocol of the Parcours, Associations, Réseau, Chronicité, Organisation, Usagers, Retour d'expérience, Soins (PARCOURS)-COVID study. The aim of this study is to evaluate the effects of reorganization of the health system on the usual care network of patients with chronic illness, which fosters and qualifies the quality and continuum of care provided. The first objective of this study is to document these patients' experiences through transformations and adaptations of their network, both in the practical dimension (ie, daily life and care) and subjective dimension (ie, psychosocial experience of illness and relationship to the health system). The second objective of the study is to understand and acknowledge these reorganizations during the COVID-19 lockdown and postlockdown periods. The third objective is to produce better adapted recommendations for patients with chronic illness and value their experience for the management of future health crisis. METHODS: The PARCOURS-COVID study is a qualitative and participatory research involving patient organizations as research partners and members of these organizations as part of the research team. Three group of chronic diseases have been selected regarding the specificities of the care network they mobilize: (1) cystic fibrosis and kidney disease, (2) hemophilia, and (3) mental health disorders. Four consecutive phases will be conducted, including (1) preparatory interviews with medical or associative actors of each pathology field; (2) in-depth individual interviews with patients of each pathology, analyzed using the qualitative method of thematic analysis; (3) results of both these phases will then be triangulated through interviews with members of each patient's care ecosystem; and finally, (4) focus groups will be organized to discuss the results with research participants (ie, representatives of chronic disease associations; patients; and actors of the medical, psychosocial, and family care network) in a research-action framework. RESULTS: The protocol study has undergone a peer review by the French National Research Agency's scientific committee and has been approved by the research ethical committee of the University of Paris (registration number: IRB 00012020-59, June 28, 2020). The project received funding from August 2020 through April 2021. Expected results will be disseminated in 2021 and 2022. CONCLUSIONS: Our findings will better inform the stakes of the current health crisis on the management of patients with chronic illness and, more broadly, any future crisis for a population deemed to be at risk. They will also improve health democracy by supporting better transferability of knowledge between the scientific and citizen communities. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/28728.

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)0.080
metaresearch head score (Gemma)0.059
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: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.088
Threshold uncertainty score0.421

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0800.059
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0050.006
Science and technology studies0.0100.006
Scholarly communication0.0050.005
Open science0.0050.006
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0880.014

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 head0.491
GPT teacher head0.696
Teacher spread0.204 · 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
GenreProtocol

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

Citations7
Published2021
Admission routes1
Has abstractyes

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