With these lines, partitions, axes and domains in mind, when a clinical practitioner is presented with a new person, whether as a patient, client, or carer ... they can, using Hodges' model (and other tools!) approach their assessment in an open and receptive manner.
This means that the information provided by the 'patient' can be readily fielded, captured whatever the context and situation.
As noted previously, my study of Hodges' model began in the late 1980s. Application in my work as a community mental health nurse, with an interest in informatics followed quite naturally(?). Primed as I was, for various reasons to carry this forward, I also carried a mathematical learning disability. At the risk of getting bogged down in my thought, use and approach to Hodges' model I need a challenge.
Mathematics is the challenge for me. It's fascinating how we have in-built 'calculators' that can help us catch a ball, and judge fairly well where to throw a ball for interception. There seems then to be an informal or naïve mathematics, at work unconsciously. Does the same apply to Hodges' model? If so, how can I isolate, and identify it?
- Is it represented somewhere, implicit in Hodges' model itself?
- Is it (once again) to be found in the user of the model?
- Is it (more likely, and obviously) a combination of these two?
- Or, is it a product of the system, or a series of systems?
I was reminded of what is a Sober toy, several years ago:
All four original purposes of Hodges' model:
- Person-centred, integrated and holistic care;
- To bridge the theory - practice gap;
- To facilitate reflection and reflective practice;
- To support curriculum development;
- are concerned with conjunction and choice, selection. So is life itself through distinction, difference, and differentiation.
Hodges' model is a selection machine, that is both fhuman and machine driven.
A clinician may obtain the referral information through an email, a history of previous contacts can be retrieved from a clinical information system; the context and purpose supporting access to the information.
A whole series of blog posts describe the role of Hodges' model to help assure parity of esteem across mental and physical health. What does this mean in practice?
For the practitioner, they take selected data from the referral, a history - if available, an initial telephone contact, a conversation with a colleague who remembers the person re-referred and starts to populate Hodges' model. What are the psychological concepts that arise? What are the physical?
If a referral in whatever form, or a database record can be viewed as a bag-of-words, then Hodges' model is a collection of care concepts. Four bags then. Sets or classes. An experienced user of Hodges' model may position care concepts that throws attention on the INDIVIDUAL↔GROUP axis. Lying between the INTRA- INTERPERSONAL and SCIENCES domains, this axis (like all the others) earns its keep. There is work to be done that is also of interest in machine learning:
'A support vector machine (SVM) is a supervised machine learning algorithm that classifies data by finding an optimal line or hyperplane that maximizes the distance between each class in an N-dimensional space.
SVMs were developed in the 1990s by Vladimir N. Vapnik and his colleagues, and they published this work in a paper titled "Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing"1 in 1995.'
https://www.ibm.com/think/topics/support-vector-machine
Strange to think that perhaps the VERTICAL axis and others in Hodges' model are not precisely S-N-E-W in their bearing? There may also be several vectors at work in fact?
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| Image: c/o https://www.ibm.com/think/topics/support-vector-machine |
The word 'naïve' has been bubbling away for a good-many years. A close colleague Silvana Bettiol, Univ. of Tasmania kindly read my draft on Hodges' model as a mathematical object, and mentioned the introduction points to Bayes theorem even if informally. Even in those initial 'clinical' encounters (and social meetings, that attend to empathy, rapport and engagement...) complex judgements are being made, beliefs tested, from what is often partial and disparate sources of information.
Checking other leads led to Frequentist and Bayesian Approaches
'Statistical inference is a series of methods used to make decisions and draw conclusions based on available data. There are two primary approaches for inference: Frequentist and Bayesian. Each framework relies on a different philosophical perspective on probability and modeling, leading to different techniques and interpretations. Each has its own strengths and drawbacks, so understanding the distinctions between them is vital for researchers, data scientists, and statisticians who aim to choose the most suitable approach for their specific analysis.'
https://www.statology.org/comparing-frequentist-and-bayesian-approaches/
More reading required and threads to run.
Earlier this week I posted re. Cromer's book -
Cromer, A. (1997) Connected Knowledge: Science, Philosophy, and Education, Oxford: Oxford University Press
Before passing the book on, p.198, Chapter 8 notes, #4:
'"Understanding" is a commonly used English word which has no precise meaning. It's sometimes taken to mean the ability to apply knowledge to new situations. In this sense, it is a very high-level skill. Benchmarks for Science Literacy says, "Learning to solve problems in a variety of subject-matter contexts, if supplemented on occasion by explicit reflection on that experience, may result in the development of a generalized problem-solving ability that can be applied in new contexts' (American Association for the Advancement of Science, 1993)." The key word here is "may." 'We really don't know how to help students develop a generalized problem-solving ability, or whether there is such an ability apart from mere knowledge of many different problem-solving strategies. Whatever the case, since we do know how to teach students to solve specific, problems. this should be the primary focus of science education' p.198.
Ack. IBM.
orcid.org/0000-0002-0192-8965
