MétaCan
Menu
Back to cohort
Record W3006928897 · doi:10.1177/0969733020901814

The patients have a story to tell: Informed consent for people who use illicit opiates

2020· article· en· W3006928897 on OpenAlexafffund
Jane McCall, J. Craig Phillips, Andrew Estafan, Vera Caine

Bibliographic record

VenueNursing Ethics · 2020
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of OttawaUniversity of CalgaryUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsInformed consentThematic analysisQualitative researchPsychologyPopulationPsychiatryHeroinMedicineFamily medicineAlternative medicineDrugSociologySocial science

Abstract

fetched live from OpenAlex

BACKGROUND: There is a significant discourse in the literature that opines that people who use illicit opiates are unable to provide informed consent due to withdrawal symptoms and cognitive impairment as a result of opiate use. AIMS: This paper discusses the issues related to informed consent for this population. ETHICAL CONSIDERATIONS: Ethical approval was obtained from both the local REB and the university. Written informed consent was obtained from all participants. METHOD: This was a qualitative interpretive descriptive study. 22 participants were interviewed, including 18 nurses, 2 social workers and 2 clinic support workers. The findings were analyzed using thematic analysis, which is a way of systematically reducing the complexity of the information to arrive at generalized explanations. RESULTS: The staff at the clinic were overwhelming clear in their judgment that people who use opiates can and should be able to participate in research and that their drug use is not a barrier to informed consent. CONCLUSIONS: It is important to involve people who use opiates in research. Protectionist concerns about this population may be overstated. Such concerns do not promote the interests of research participants. People who use heroin need to be able to tell their story.

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)0.349
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.566
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.349
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
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 head0.676
GPT teacher head0.595
Teacher spread0.081 · 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

Citations8
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueNursing EthicsSame topicEthics in Clinical ResearchFrench-language works237,207