The practice and teaching of palpation of the head and neck: A scoping review
Bibliographic record
Abstract
Objective: To explore how palpation of the head and neck is practiced and taught.Methods: The scoping review methodology was guided by Arksey and O’Malley’s five-stage approach. Three experienced and independent reviewers searched nine databases according to a predetermine inclusion and exclusion criteria.Results: A total of 15 articles from medicine, chiropractic and dentistry published between 1987 and 2016 were included. Two overarching themes emerged, a Cartesian and a Pragmatic perspective in practicing and teaching palpating of the head and neck. Although both perspectives are valuable, we advocate to practice and teach palpation of head and neck from a Pragmatic perspective particularly with the increase use of ultrasound technology to detect masses. A pragmatic perspective takes into account the patient’s context, the ethics of care and highlights the importance of health care providers fostering interpersonal relationships with others during physical assessment.Conclusions: Although nursing studies were absent from this review we believe nurses play a vital role when they are aware of the Cartesian and Pragmatics perspectives when practicing and teaching head and neck palpation as part of a physical assessment. Learning how other disciplines are practicing and teaching head and neck palpation skills will improve interdisciplinary collaboration.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".