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Record W4400799443 · doi:10.1177/10497323241245340

The Eternal Present: A Photovoice Study of the Experience of Geriatrics Residents During the COVID-19 Pandemic

2024· article· en· W4400799443 on OpenAlexaff
Juan Pablo Negrete‐Najar, Adina Radosh Sverdlin, Alejandro Arreola Rodríguez, Ana Patricia Navarrete‐Reyes

Bibliographic record

VenueQualitative Health Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhotovoiceFeelingPandemicGeriatricsIsolation (microbiology)BurnoutCoronavirus disease 2019 (COVID-19)Perspective (graphical)PsychologyTheme (computing)MedicineMental healthAdaptation (eye)Medical educationNursingSocial psychologyClinical psychologyPsychiatryDisease

Abstract

fetched live from OpenAlex

During the COVID-19 pandemic, medical residents had the task of being the frontline of the response, being exposed to high risk of infection, increased clinical duty, and long and irregular working hours in highly restricted environments, increasing their levels of stress. We sought to expose the experiences of a group of geriatrics residents during this period of change in their professional and personal lives through the photovoice methodology. Thirteen participants were recruited and had 2 weeks to take photographs. The photographs were discussed in group meetings; the content of the conversations was transcribed and analyzed using interpretive description. Sixteen themes were identified. They were divided into personal life (11 themes) and life as a resident (5 themes). Adaptation was the main theme that came into discussion. The photographs and themes show how life changed for the participants, having a feeling of isolation, especially from their families, and highlighting their experiences as a team and community. While the pandemic, particularly at its beginning, was a period of uncertainty and a heavy load of work, it also provided learning and experience to this group of young physicians, which should not hide the fact that mental health concerns and burnout were a common situation. An online gallery was created which is publicly accessible.

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)0.104
metaresearch head score (Gemma)0.048
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch, Science and technology studies
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 score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1040.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0040.004
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.925
GPT teacher head0.807
Teacher spread0.118 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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