Introduction to Special Section on Pseudoscience in Psychiatry
Notice bibliographique
Résumé
As Nobel prize-winning physicist Richard Feynman reminded us, first principle is that you must not fool yourself, and you are the easiest person to fool.1, p 12 One crucial principle in psychiatry is that all of us, no matter how intelligent or well-trained, are susceptible to being duped by specious claims. Research reveals, at best, modest and often negligible correlations between measures of intelligence and critical thinking skills, suggesting that these 2 domains are largely distinct.2 Nevertheless, because of a phenomenon known as bias blind spot, whereby most of us are keenly aware of others' mental shortcomings yet largely oblivious to our own,3 we may overestimate our capacities to distinguish dubious from well-supported psychiatric claims (for reviews of widespread biases and other errors in psychiatry, see Croskerry4 and Crumlish and Kelly5).A careful consideration of errors in thinking is germane to psychiatry and related fields because of the continuing insinuation of pseudoscientific claims into myriad domains of mental health practice.6 Pseudoscientific claims display the superficial trappings of science but lack its substance. As a consequence, they can readily fool nonspecialists-and even specialists, on occasion-into believing that they are well-supported by evidence. In contrast to developed sciences, pseudosciences tend to lack methodological and procedural safeguards against confirmation bias, the deeply entrenched tendency to seek out evidence consistent with one's hypotheses and to deny, dismiss, or distort evidence that is not.7 Such safeguards include randomization to conditions in the case of experimental designs; placebo controls; blinded designs; pre- and post-test measures with demonstrated reliability, construct validity, norms, and standardization; and rigorous peer review.8 These safeguards are far from foolproof and do not eliminate all sources of medical error.9 Nevertheless, they are widely accepted as desiderata in psychiatric research and are crucial bulwarks against commonplace errors in clinical inference.Although the boundaries separating pseudoscience from science are fuzzy,10 pseudosciences are characterized by several warning signs-fallible but useful indicators that distinguish them from most scientific disciplines. Such warning signs include an emphasis on confirmation rather than refutation of hypotheses (weighing hits more than misses), overuse of ad hoc hypotheses (after-the-fact escape hatches or loopholes) for explaining away negative findings, absence of self-correction in the face of repeated negative findings, placing the burden of proof on skeptics rather than on proponents of assertions, expansive claims that greatly outstrip the available research evidence, overreliance on anecdotal evidence (anecdata), evasion of systematic peer review, and the use of scientific-sounding but largely vacuous terminology (for example, receptors of the neuro networks with progressively lower valences11, p 318).12,13 In contrast to most accepted medical interventions, which are prescribed for a circumscribed number of conditions, many pseudoscientific techniques lack boundary conditions of application. For example, some proponents of Thought Field Therapy, an intervention that purports to correct imbalances in unobservable energy fields, using specified bodily tapping algorithms, maintain that it can be used to treat virtually any psychological condition, and that it is helpful not only for adults but also for children, dogs, and horses.14No indicator of pseudoscience should be used in isolation to disqualify a claim, because some scientific research programs make use of them as well. For example, ad hoc hypotheses play a legitimate role in science, especially when invoked judiciously. In most mature sciences, such hypotheses tend to enhance the theory's content, predictive power, or both. In contrast, in pseudosciences, ad hoc hypotheses are typically introduced as desperate measures to explain away contrary findings, and rarely enhance the theory's substance or capacity to generate successful predictions. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,008 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,096 | 0,046 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».