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Reforming private drug coverage in Canada: Inefficient drug benefit design and the barriers to change in unionized settings

2014· article· en· W2001463610 on OpenAlexaffabout
Sean O’Brady, Marc‐André Gagnon, Alan Cassels

Bibliographic record

VenueHealth Policy · 2014
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsCarleton UniversityUniversity of VictoriaUniversité de Montréal
Fundersnot available
KeywordsDrugBusinessMedicinePharmacology

Abstract

fetched live from OpenAlex

Prescription drugs are the highest single cost component for employees' benefits packages in Canada. While industry literature considers cost-containment for prescription drug costs to be a priority for insurers and employers, the implementation of cost-containment measures for private drug plans in Canada remains more of a myth than a reality. Through 18 semi-structured phone interviews conducted with experts from private sector companies, unions, insurers and plan advisors, this study explores the reasons behind this incapacity to implement cost-containment measures by examining how private sector employers negotiate drug benefit design in unionized settings. Respondents were asked questions on how employee benefits are negotiated; the relationships between the players who influence drug benefit design; the role of these players' strategies in influencing plan design; the broad system that underpins drug benefit design; and the potential for a universal pharmacare program in Canada. The study shows that there is consensus about the need to educate employees and employers, more collaboration and data-sharing between these two sets of players, and for external intervention from government to help transform established norms in terms of private drug plan design.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.030
GPT teacher head0.340
Teacher spread0.309 · 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 designQualitative
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

Citations13
Published2014
Admission routes2
Has abstractyes

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