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Record W2621050123

Canadian Academic Health Science Centres: Developing a Framework to Address Compensation, Academic Productivity, Performance and Quality

2017· dissertation· en· W2621050123 on OpenAlexfundaboutno aff
Wendy Ulla Kubasik

Bibliographic record

VenueTSpace (University of Toronto) · 2017
Typedissertation
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsnot available
FundersOffice of International Science and EngineeringUniversity of Toronto
KeywordsProductivityQuality (philosophy)Compensation (psychology)Health scienceEngineering managementBusinessPolitical scienceMedical educationMedicinePsychologyEngineeringEconomic growthEconomics
DOInot available

Abstract

fetched live from OpenAlex

Academic leaders and policymakers are faced with a series of difficult and complex decisions around methods of assessing and compensating academic physicians with regards to their performance. Despite the introduction of Alternative Funding Plans by many provincial governments, concerns around the reliance on clinical revenues to fund components of the medical school’s academic mission, increased competition for research grants, physician compensation costs coupled with the rising costs of education and health care delivery, the prevalence of institutional ranking systems and pressures to demonstrate accountability, have all endured. While anecdotal evidence on the types of compensation and academic productivity frameworks exists, studies or literature on the prevalence of these models and their effectiveness within Canadian Academic Health Science Centres (AHSCs) are limited. This dissertation uses an emergent, multi-phase, mixed methods research methodology to explore the methods used to compensate and assess Canadian academic physicians. This dissertation provides methodological, empirical and theoretical contributions to knowledge. Empirically, the phase 1 national survey of academic leaders achieved a 61% response rate (39 respondents), and confirmed that AHSCs use various systems to compensate, assess and incentivize academic physicians. Phase 2 consisted of three mixed methods case studies (three institutions, three provinces, four clinical academic departments and five different compensation models) to analyze the academic productivity of 150 academic physicians over a five-year period between 2006 and 2011. Qualitative data were gleaned from the interviews of academic leaders. Methodologically, a new quantitative and bibliometric assessment model – the K-MAAP© – was developed to analyze the research outputs and impact of those included in the sample, calculate research return-on-investment and efficiency. Theoretically, elements of contract theory, incentives, and behavioural economics were utilized. Behavioural economics is an emergent, hybrid field that seeks to reconcile the gap between standard economic theories and cognitive theories by showing how people actually do behave, and not just how they are expected to behave. A new type of incentive which emerged during the case study research – called a “placebo incentive” – was also described. Several policy implications are presented including methods to fund AHSCs; methods to fund academic physicians; the use of incentives in academic medicine; accountability, metrics and productivity assessments; and systems-level issues related to the alignment of incentives and compensation programs. This information is important for policy makers, AHSCs, hospitals, physician associations, practice plans and academic leaders as they seek research addressed at understanding the efficacy of the models used to compensate, assess, motivate and reward academic physicians for scholarly tasks.

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)0.035
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score0.897

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0200.025
Science and technology studies0.0190.025
Scholarly communication0.0230.010
Open science0.0070.011
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.141
GPT teacher head0.451
Teacher spread0.310 · 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 designTheoretical or conceptual
DomainIncentives
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
Published2017
Admission routes2
Has abstractyes

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