Consulting with NLP: Neuro-linguistic Programming in the Medical Consultation
Bibliographic record
Abstract
Consulting with NLP is written for clinicians, and especially general practitioners, who are concerned about their effectiveness or their eagerness when consulting patients. It begins with a warning: you will have to do more than simply skim through 300 pages. You will need to stop, reflect, explore your own thinking processes, carry out exercises, encourage others to help you and even, at times, be prepared to look and feel a fool. The promise if you do this? A new, refreshed approach to working with your patients, new mastery of your work as a doctor, a new ability to look after yourself amidst the stresses of a career in healthcare and even new developments in your own personal approach to life. Such an introduction suggests that this is a book with big ambitions. Indeed it is. Lewis Walker, having gained considerably by applying the concepts of neuro-linguistic programming to his own work, wants to share the good news. After a brief introduction he shows how these concepts can be used in the different stages of a consultation, making use of the Calgary-Cambridge model associated with Silverman, Kurtz and Draper. This enables him to link new ideas with a well-established structure, at least in the sphere of general practice communication. He pays special attention to difficult situations such as managing anger and giving bad news, and concludes with a whole chapter on personal development. Throughout he is able to introduce complex notions such as ‘meta-models’ and ‘perceptual positions’ in a fresh and easy to understand way. There are many practical examples from everyday experience and every effort is made to express ideas in ways that will be relevant to readers and to show why it is important to try out the exercises as you progress. To quote the author: ‘my goal throughout this book is to present the information in so many ways that you come to find yourself automatically using it’. So we have here a well-written, practical working book addressing a major concern to both patients and professionals in modern healthcare. Here is a book that should remotivate and enthuse members of a profession who have lost their way or fear burn-out. With so much in its favour, why did I end up feeling unhappy and dissatisfied with it? Was it the lack of much academic, as against anecdotal, evidence for the outcomes suggested? Was it simply annoyance with a writing style that constantly italicized important words? Was it the jarring use of outdated, derogatory and doctor-centred terms, such as ‘heartsink’ patients? All these may have contributed, but on reflection it was probably that there appeared to be a lack of congruence with some of my own beliefs and values, sense of identity and purpose. In particular, the book felt more doctor-centred than patient-centred, and the arguments felt more like behavioural manipulation than a free personal relationship. In the middle of the book there is a brief paragraph on ‘respecting the unique individual in front of you’, but this seemed less significant than the ‘fun’ of using a ‘polarity response’ to achieve an outcome. With the emphasis on the non-verbal, the anchoring and the patterning, one felt more like Pavlov's dog than a human created with free thought and will. This is, of course, a personal reaction. The best way to judge would be to read the book yourself. I would agree with Lewis Walker that you will not be the same at the end.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".