Assessing the Competence of Physical Therapists who Perform Airway Suctioning with Adults
Bibliographic record
Abstract
Airway suctioning is a treatment technique used to remove retained secretions from the lungs. Given the higher-risk nature of this technique, it is important to ensure the competence of health care professionals who perform it. This dissertation explores the process used to develop and evaluate a new measure intended to assess the clinical competence of physical therapists who perform suctioning with adults, the Physical Therapy Competence Assessment for Airway Suctioning (PT-CAAS). Assessing the competence of physical therapists is of interest because they have fewer opportunities to perform suctioning in practice compared to other health care professionals, such as nurses and respiratory therapists. Consequently, they may be at greater risk for erosion of competence. The first study used a scoping review methodology to explore the nature and extent of measures to assess the competence of health care professionals who perform suctioning with adults. In that review, we failed to identify any existing measures that would be appropriate for current use with physical therapists who perform suctioning within the Canadian health care context. In the second study, expert consensus was used to develop an initial version of the PT-CAAS and assess its sensibility. In that study, we found preliminary evidence in support of the PT-CAAS’s face and content validity. In the third study, qualitative interviews were used and we found further evidence to support the PT-CAAS’s content validity. In the fourth study, scoring rules were developed and the inter-rater and intra-rater reliability of the PT-CAAS were assessed. We found evidence of moderate to good inter-rater reliability and good intra-rater reliability; however, the precision of the reliability estimates was a concern. Based on our findings, the initial version of the PT-CAAS was revised. Further assessments of the PT-CAAS’s reliability and validity are recommended.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".