Developing the Therapeutic Use of Self in the Health Care Professional through Autoethnography: Working with the Borderline Personality Disorder Population
Bibliographic record
Abstract
Frequently stigmatized, misdiagnosed, improperly treated, and discounted is the suffering of the patient with Borderline Personality Disorder (BPD), and it can be a serious, agonizing, tenacious, and draining mental illness. Present-day research illustrates that patients with BPD are in fact the largest consumers of mental health services, utilizing every treatment genre more frequently and in greater quantities than any other mental health taxonomy. They experience more complex and destructive symptoms, more perpetual misery and encumbrance, an unpredictable usage of outpatient services, and extensive treatment modalities and psychiatric admissions. A review of current literature reveals this consistent notion: the attitudes of health care professionals toward patients diagnosed with this elaborate disorder tend to be disparaging. The aim of this article is to critically analyze the prospect that autoethnography (or narrative research) is a strategic, useful tool for mental health professionals to improve empathy and identification with patients suffering with BPD. As a qualitative research method, autoethnography is advantageous for creating connections between care provider and patient. It can deepen their mutual and divergent experiences while generating empirical knowledge from the professional's narrative reflection and through the therapeutic use of self with the patient.
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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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".