Patient centered method and soundscape—A bridge between clinicians and acousticians
Bibliographic record
Abstract
The Patient Centered Method (PCM) and Soundscape have much in common including their emergence about 60 years ago based on the work of Balint and Kryter, respectively. Both place the patient or person at the center of management of clinical illness or noise annoyance. PCM requires that the patient perceive that they have experienced meaningful care, communication, and common ground in clinical encounters. The evaluation focuses on the patient’s life context and their perception of disease or the “illness experience.” When PCM is accomplished, the result is higher satisfaction, better outcomes of chronic diseases, fewer tests, and referrals and attendant lower costs (Stewart et al., 2000). Soundscape, a term coined by Shafer in 1977 also places the person in center, in the context of their acoustic environment, emphasizes their perception of noise as the “New Experts” (Bray 2012). According to Bray exposed people are “objective measuring instruments whose reports and experiences must be taken seriously and quantified by technical measurements.” This paper will explore the congruence of PCM and Soundscape and the necessity of this approach in evaluating the experience of those exposed to wind turbine acoustical energy.
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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 | 0.036 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".