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Record W4309791309 · doi:10.18192/aporia.v14i2.6415

Voix infirmières pendant la COVID-19 : Une analyse de la couverture médiatique au Canada

2022· article· fr· W4309791309 on OpenAlexafffundvenueabout
Marilou Gagnon, Amélie Perron

Bibliographic record

VenueAporia · 2022
Typearticle
Languagefr
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of OttawaUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of VictoriaUniversity of Ottawa
KeywordsHumanitiesCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Political science2019-20 coronavirus outbreakPhilosophyMedicineVirology

Abstract

fetched live from OpenAlex

Même si l’on reconnaît généralement que le personnel infirmier et les enjeux liés aux soins infirmiers sont sous-représentés dans les médias, l’inverse est également vrai pendant des crises sanitaires d’envergure comme l’Ébola et le SRAS (syndrome respiratoire aigu sévère). Nous constatons la manifestation de ce même phénomène pendant la pandémie de COVID-19, pendant laquelle le personnel infirmier et les enjeux liés aux soins infirmiers sont le sujet d’une forte couverture médiatique au Canada et à l’international. Pour mieux comprendre cette couverture médiatique, nous avons analysé le contenu d’articles de presse canadiens publiés en anglais et en français au cours des cinq premiers mois de la pandémie de COVID-19. Le présent article présente les résultats de notre analyse et identifie les grandes leçons tirées de celle-ci. Ces résultats représentent selon nous un important point de départ pour mieux comprendre l’agentivité du personnel infirmier et le savoir-faire démontré par ce dernier pendant les premiers mois de la pandémie.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.028
GPT teacher head0.388
Teacher spread0.360 · 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.

Study designNot applicable
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
Published2022
Admission routes4
Has abstractyes

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