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Record W2996766824

[no title]

2017· article· fr· W2996766824 on OpenAlexaboutno aff
Vanessa Slobogian, Jennifer Giles, Tiffany Rent

Bibliographic record

VenuePubMed · 2017
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesEthnologySociologyArt
DOInot available

Abstract

fetched live from OpenAlex

Dans le milieu de l’oncologie, l’utilisation des médias sociaux à titre personnel est un sujet épineux. Les renseignements personnels des patients et des fournisseurs de soins y sont facilement accessibles, ce qui compromet la distance professionnelle. Le présent article d’opinion donnera un aperçu de la responsabilité qui revient aux infirmières en oncologie de conserver leur distance professionnelle et traitera des conséquences possibles sur leur réputation professionnelle de la consultation de renseignements personnels en ligne par les patients et leurs familles. L’article expose également le travail réalisé par le groupe d’étude sur les médias sociaux dans la pratique infirmière avancée du programme de transplantation d’hémato-oncologie de l’Hôpital pour enfants de l’Alberta; une revue de la littérature sur le sujet et l’élaboration d’un sondage mené auprès du personnel pour explorer les perceptions et pratiques quant à l’utilisation des médias sociaux par les professionnels de la santé sont aussi abordés. Enfin, des études de cas illustrant les défis couramment posés par les médias sociaux ainsi que des propositions d’activités pour favoriser le transfert des connaissances sont présentés.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.969
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0310.004

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 head0.302
GPT teacher head0.419
Teacher spread0.117 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venuePubMed→Same topicSocial Media in Health Education→French-language works237,207→