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Record W2111891723 · doi:10.12927/hcpap..18682

Mapping Out the Territory

2007· letter· en· W2111891723 on OpenAlexaffvenueabout
Linda O’Brien‐Pallas

Bibliographic record

VenueA Nudge Too Far? A Nudge at All? On Paying People to Be Healthy · 2007
Typeletter
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWorkloadStaffingWorkforceWork (physics)Scale (ratio)Intervention (counseling)NursingHuman resourcesBusinessPublic relationsPsychologyMedicinePolitical scienceManagementEngineeringGeographyEconomics

Abstract

fetched live from OpenAlex

This commentary is a response to the paper "Healthy Workplaces for Health Workers in Canada: Knowledge Transfer and Uptake in Policy and Practice," in which Shamian and El-Jardali describe completed research and policy directions to improve work-life practices and create healthy workplaces in the environments where health workers are employed. Two issues that are raised in the discussion are focused on, the first one being health of the workforce and the second concerning workload measurement and work overload. Evidence from two recently completed studies is provided to demonstrate the importance of monitoring the health of caregivers and the need for development of new workload measurement systems. Such progress requires large-scale studies to help us understand the correlates of staff satisfaction, staffing outcomes and workplace demands. Most importantly, evaluation of policy intervention in Canada has been limited; therefore, once fiscal and human resources are directed to policy initiatives, these actions need to be formally evaluated.

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)0.006
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.662
Threshold uncertainty score0.681

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0180.012
Scholarly communication0.0070.007
Open science0.0050.006
Research integrity0.0380.043
Insufficient payload (model declined to judge)0.0180.005

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 head0.080
GPT teacher head0.389
Teacher spread0.308 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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
Published2007
Admission routes3
Has abstractyes

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