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Record W4317886106 · doi:10.1177/23743735231151770

Seeking Care for Long COVID: A Narrative Analysis of Canadian Experiences

2023· article· en· W4317886106 on OpenAlexafffundabout
Julia G. Kaufmann, Odette N. Gould, Vett K. Lloyd

Bibliographic record

VenueJournal of Patient Experience · 2023
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsMount Allison University
FundersNew Brunswick Innovation Foundation
KeywordsNarrativeHealth careCoronavirus disease 2019 (COVID-19)PandemicPsychologyNarrative inquiryLegitimacyHealthcare systemMedicineNursingPolitical scienceDisease

Abstract

fetched live from OpenAlex

The goal of this study was to explore the experiences of individuals seeking care for long COVID-19 in the Canadian healthcare system. Recorded virtual interviews were carried out with 8 participants and narrative analysis was used to examine the stories produced and identify the central narratives that defined participants' experiences. Care-seeking experiences were characterized by (1) often debilitating multi-system symptoms for which little information about prognosis was available and no effective treatments were provided, (2) compounded by the frustration of trying to convince family, friends, and health care practitioners of the legitimacy of their illness, (3) access to medical care was severely limited by the global pandemic and associated higher thresholds for care, (4) like others suffering from complex, multi-system conditions, people with long COVID are often struggling with a health-care system ill-suited for dealing with long-term and possibly chronic conditions. To make system-level improvements to better serve those with chronic conditions, it is critical that we understand the care-seeking experiences of chronic illness patients, including the unique experiences of those with long COVID.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.073
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.341
Teacher spread0.319 · 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 teacher head, 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

Citations5
Published2023
Admission routes3
Has abstractyes

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