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Record W2591797623

CLINICAL DECISION MAKING IN PARAMEDICINE

2017· dissertation· en· W2591797623 on OpenAlex
Michael Eby

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMacSphere (McMaster University) · 2017
Typedissertation
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsnot available
Fundersnot available
KeywordsClinical decision makingMedicineIntensive care medicine
DOInot available

Abstract

fetched live from OpenAlex

Title: Clinical Decision Making in Paramedicine Author(s) & affiliation(s): Michael Eby – McMaster University, Hamilton, ON, Canada Sandra Monteiro – McMaster University, Hamilton, ON, Canada Geoffrey Norman – McMaster University, Hamilton, ON, Canada Walter Tavares – McMaster University, Hamilton, ON, Canada Background: Paramedics are frequently required to make rapid decisions in an uncontrolled, dynamic environment, often with limited diagnostic information. In Ontario, paramedic practice is based on a set of provincial medical directives that provide diagnostic and treatment criteria. Unsupervised deviation from these directives is classified as a form of error and highly discouraged. To date, there is little known about how years of clinical experience or level of certification affect the way these medical directives are used. The purpose of this study was to examine the relationship between paramedic experience, training and accuracy of treatment decisions when faced with patients who meet and fall outside of the existing medical directives. Methods: Thirty-one participants (16 experienced / 15 novice) were recruited from two paramedic services in Ontario. “Experienced” was defined as in-practice for 5 years or more. Participants were presented with 9 scenarios; in 6 scenarios, the patient presentation fit within the existing directives, while in 3 scenarios, the patient presentation fell outside the medical directives. Multiple-choice responses were used to capture participants’ decisions to treat or not treat the patients. Responses were scored and submitted to a mixed-factorial ANOVA to evaluate differences in accuracy between case types, years of experience and level of training. Results: There was a significant effect of case type (p < 0.004). Accuracy was lower when the patient presentation did not meet the criteria of the medical directive (76.34% (CI = 67.15% to 85.53%) vs. 98.35% (CI = 96.55% to 100%) when they did. There was no effect of years of clinical practice or level of certification. Conclusion: The results suggest both novice and experienced paramedics are able to accurately apply medical directives, however, there is a significant decrease in accuracy when the patient presentation does not fit one. This variation in practice may have a significant impact on patient safety, and further research is required to determine what factors may be causing this decreased accuracy.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.820
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0990.001

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 head0.430
GPT teacher head0.540
Teacher spread0.110 · 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