MétaCan
Menu
Back to cohort
Record W2154119107 · doi:10.5430/jha.v3n5p88

Historicity and historiography of being-a-nursing- student in the construction of care in Heidegger

2014· article· en· W2154119107 on OpenAlexvenueno aff
Silvana Silveira Kempfer, Telma Elisa Carraro, Marta Lenise do Prado

Bibliographic record

VenueJournal of Hospital Administration · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth, Nursing, Elderly Care
Canadian institutionsnot available
Fundersnot available
KeywordsHermeneuticsHistoricity (philosophy)HistoriographyContext (archaeology)ComprehensionInterviewPhenomenonNursingInterpretation (philosophy)Hermeneutic phenomenologyPsychologyNursing careQualitative researchEpistemologyPedagogyMedicineSociologyLived experienceHistoryPhilosophyPsychoanalysisLinguisticsSocial sciencePolitical science

Abstract

fetched live from OpenAlex

Objective: Reveal the experience of being-a-nursing-student as it relates to care. Method: Phenomenological qualitative research. Data were collected in March and May, 2011 by interviewing seven nursing students at the Federal University of Santa Catarina. The interviews were analyzed using heideggerian hermeneutics in three steps: Pre-comprehension, comprehension, and the interpretation of the participants’ responses. Results: Reflecting on the historiographical context of being-a-nursing-student, the respondents realized that they are immersed in the family context throughout their career, and described their experiences as temporal discoveries in their life. By unveiling their historicity in the phenomenon of care, the students were able to recognize themselves in this process, interacting with each other and with the practical situations in which moments of care were required. This process has made it possible to conceive a vision of care from their past experience. Conclusion: To nursing students, one concept of care involves reflecting continuously on oneself and on the other.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.012
GPT teacher head0.364
Teacher spread0.352 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Hospital AdministrationSame topicHealth, Nursing, Elderly CareFrench-language works237,207