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Record W3208705685 · doi:10.1177/08404704211054142

Recalibrating healthcare to create a more equitable post-pandemic work environment

2021· article· en· W3208705685 on OpenAlexaff
W. Glen Pyle, Frances C. Roesch

Bibliographic record

VenueHealthcare Management Forum · 2021
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsMcMaster UniversityUniversity of Guelph
Fundersnot available
KeywordsEquity (law)PandemicInclusion (mineral)BusinessWork (physics)Health carePublic relationsTeamworkDiversity (politics)PerceptionCoronavirus disease 2019 (COVID-19)PsychologyPolitical scienceMedicineManagementEconomic growthEconomicsEngineering

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has accelerated the need for flexible arrangements, including asynchronous work and working from home. These arrangements may be necessary to comply with public health directives and are manageable when few other options exist. It can be difficult to lead in an environment when team members have divergent core working hours and are not available for collaboration. This can be compounded by the perception of inequitable treatment of employee needs or preferences by management, which can further strain team dynamics. As the pandemic eases, it may be difficult for all employees to revert to a fully on-site arrangement; some may be unable and others unwilling. Leaders will need to consider ethical issues in reaching organizational goals in this new reality. Equity, diversity, and inclusion principles will be critical when balancing the needs of the individual and the team. Supportive arrangements and a culture of inclusion will be key to retaining top talent.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.660
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.061
GPT teacher head0.388
Teacher spread0.327 · 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 designObservational
Domainnot available
GenreCommentary

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

Citations6
Published2021
Admission routes1
Has abstractyes

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