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Record W4226316032 · doi:10.1177/23743735221089698

A Qualitative Study of the Impact of the COVID-19 Pandemic on a Sample of Patients With Chronic Pain

2022· article· en· W4226316032 on OpenAlexaff
Ola Mohamed Ali, Victoria Borg Debono, Jennifer Anthonypillai, Eleni G. Hapidou

Bibliographic record

VenueJournal of Patient Experience · 2022
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMcMaster University Medical CentreMcMaster UniversityWestern University
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSample (material)Chronic painMedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Internal medicineVirologyPhysical therapyDiseaseOutbreak

Abstract

fetched live from OpenAlex

This qualitative phenomenological study examined the impact of the COVID-19 pandemic on the lives of patients living with chronic pain. Patients referred to an intensive interdisciplinary pain management program between June 2020 to June 2021 were asked, "How did the COVID-19 pandemic affect your life?" as part of their interdisciplinary assessment. Ninety patients (50 Veterans, 40 civilians) provided comments to this question, which were independently organized into themes using an inductive approach by 4 researchers. Nine main themes emerged: (1) changed psychological state, (2) minimal to no effect, (3) affected personal life activities, (4) changes in accessing care, (5) changes in work/education situation, (6) changes in family dynamics, (7) experiencing more annoyances, (8) COVID-19 pandemic is a barrier to making positive changes, and (9) got COVID-19. Themes are consistent with topics of interest in light of this ongoing, global stressor. Most commonly reported themes reflected changes in psychological well-being and changes in access to care, highlighting similarities between life with chronic pain and life under the pandemic for this group.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.034
GPT teacher head0.388
Teacher spread0.354 · 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 designObservational
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

Citations8
Published2022
Admission routes1
Has abstractyes

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