The Future of Humanistic/Existential Psychology: A Commentary on David Elkins’s (2009a) Critique of the Medical Model
Bibliographic record
Abstract
In the increasingly competitive market place of mental health providers, where do we, the humanistic/existential psychologists (HEP), position ourselves? With positive psychotherapists and happiness coaches gaining grounds in the domain of personal growth, and neuroscientists and mindful meditation dom-inating the field of spirituality, in what areas can we stake out a claim of being a major player? What are the compelling characteristics of our brand of psychotherapy? These questions swirled in my head as I pondered over David Elkins’s (2009a) provocative article. Basically, I agree with Elkins’s case against the medical model and his critique of the restrictive and biased nature of evidence-supported treatment (Elkins, 2007, 2008). I can also fully under-stand his displeasure toward the health insurance industry. But here is our conundrum: We may be right on psychological, therapeutic, methodological, ethical, and moral grounds, but we still fail to gain wide acceptance by main-stream psychology. The challenge confronting us is how to overcome this barrier without compromising our core convictions. I differ from David more on matters of strategy and stance than substantive issues. For both pragmatic and theoretical reasons, I prefer a more open and inte-grative stance in the spirit of Kirk Schneider’s (2008) existential-integrative psychotherapy. We also need to develop a new coordinated strategy to fulfill
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.042 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.042 | 0.066 |
| 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".