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Record W4205213707 · doi:10.2345/0899-8205-56.1.1

Seven Ways to Inspire Innovation in the Health Technology Industry

2022· article· en· W4205213707 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBiomedical Instrumentation & Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsMcMaster UniversityPublic Health Ontario
Fundersnot available
KeywordsCreativityWork (physics)Psychological interventionOrganizational cultureIndustry 4.0Information technologyTechnology innovation

Abstract

fetched live from OpenAlex

This article explores ways in which technological innovation can be bolstered in organizations that operate in the health technology industry. We present seven interventions at the team level (employee empowerment, servant leadership, hiring innovators, and scheduling time for innovation) and organizational level (intrapreneurship, flat management, and allowing for failure) that organizations can use to encourage and inspire innovation among employees. Given the increasingly dynamic nature of work within the health technology fields, in terms of both manufacturing processes and clinical developments, creating a culture of innovation and creativity and emboldening employees to regularly engage in such behaviors within these workplaces are critical.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.008
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.275
Teacher spread0.253 · 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