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Record W4386107610 · doi:10.1177/23333936231192000

Emotional Labor of Nurses and Phlebotomists in a New Source Plasma Collection Site During the COVID-19 Pandemic

2023· article· en· W4386107610 on OpenAlexafffundabout
Kelly Holloway

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsCanadian Blood Services
FundersHealth CanadaCanadian Blood ServicesAustralian Government
KeywordsScholarshipBlood collectionPandemicCoronavirus disease 2019 (COVID-19)Data collectionWork (physics)PsychologyMedicineSociologyPolitical scienceMedical emergencyInternal medicineEngineeringSocial science

Abstract

fetched live from OpenAlex

As uses of plasma-derived medical products increase globally, so does the demand to collect plasma from donors. There is evidence that positive interactions with center staff motivate plasma donors to return. This paper reports on a focused ethnography investigating experiences of nurses and phlebotomists in one of Canadian Blood Services' first source plasma collection center during the COVID-19 pandemic. Participants found the transition from whole blood collection to source plasma amid a global pandemic challenging, but they adapted by coming together as a team, and then worked to put the donor experience first. Their experience resonates with scholarship on emotional labor. As blood services worldwide attempt to increase source plasma collection, there is a need to understand care work that nurses and phlebotomists perform on the front-line. This study offers insight into how blood services can support staff in plasma operations by recognizing emotional labor.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.063
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.140
GPT teacher head0.466
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.

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

Citations2
Published2023
Admission routes3
Has abstractyes

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