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Record W1600155305 · doi:10.1177/070674370805300301

Qualitative Research in Psychiatry

2008· letter· en· W1600155305 on OpenAlexaffvenueabout
David L. Streiner

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2008
Typeletter
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsBaycrest HospitalUniversity of Toronto
Fundersnot available
KeywordsWonderIntrospectionPsychologyQualitative researchPositivismMultidisciplinary approachCurriculumEpistemologySocial scienceSocial psychologySociologyPedagogyCognitive psychology

Abstract

fetched live from OpenAlex

Can J Psychiatry 2008;53(3): 135-136 You are entitled to wonder why the Guest Editor for an In Review section on qualitative research is the Statistical Associate Editor for The Canadian Journal of Psychiatry. It is a valid question. After all, as with most clinical psychologists trained in the 1960s and 1970s, I was brought up in a positivist tradition-if it cannot be measured, it does not exist. My curriculum, which consisted of more than 20 courses, was about equally divided between clinically-oriented topics and research- oriented ones. Despite this heavy emphasis on methodology, the word research was synonymous with the term quantitative research. Indeed, the adjective itself was never used, because no one on faculty could even conceive of research that did not involve quantification. We did learn about studies that used introspection to examine people's perceptions, but that was in a course on the history of psychology, and was used to demonstrate how far the science of psychology had progressed since the end of the 19th century, in that more precise instruments had replaced reliance on the reporting of subjective experiences. It was only when I became involved in a series of multidisciplinary studies of the effects of environmental hazards1 that I began to appreciate what the qualitative approach can add to the study of various phenomena. In one study, for example, the quantitative component clearly showed that people's anxiety levels regarding the adverse effects of a new landfill site decreased markedly in the months following its opening, but could not tell us why this happened. It was only when we followed up the large, quantitative study with more directed focus groups and in-depth interviews that the reasons emerged.2 Since that time, I have become a strong advocate of using qualitative and quantitative approaches individually and together,3 especially in the design of new measurement tools.4,5 In a way, this evolution parallels that of medicine in general, and psychiatry in particular, with those involved having ambivalent feelings toward qualitative research. Both began in times before there was a felt need, or even the ability, to quantify everything, and the lack of quantification was not equated with the absence of science. The case studies of Freud and his followers are superb precursors of qualitative studies, and many of the leading psychiatrists of the first half of the 20th century, such as Dr Alec Leighton, were also trained in anthropology. In the second half of the last century, though, the quest for greater respectability in psychiatry led to abandonment of so-called softer approaches and the adoption of quantitative methods, marked by large sample sizes, more objective measures, and more reliance on statistics. Indeed, most psychiatric journals abandoned publishing case reports entirely and it was difficult to find any articles that used qualitative methods in their pages. However, the pendulum has begun to swing back and psychiatry has again started to incorporate qualitative approaches in answering questions. This change in outlook has also been mirrored within the granting agencies that support health research. When I began sitting on Medical Research Council (MRC) review panels in 1997, it was extremely difficult for any qualitative research proposals to get funded. Few, if any, of the reviewers around the table had any experience with, or sympathy for, qualitative approaches. The proposals were looked at from a quantitative orientation and, needless to say, they did not pass muster. The sample sizes were ridiculously small in comparison with the usual studies that came to the panel and the words reliability and validity were nowhere to be found in the protocols. Obviously, this was not science as the term was conceptualized by the reviewers. Within a few years, though, around the time that the MRC broadened its mandate and changed into the Canadian Institutes of Health Research, this attitude had changed dramatically. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.143
metaresearch head score (Gemma)0.161
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.143
Threshold uncertainty score0.758

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1430.161
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0070.009
Science and technology studies0.0110.027
Scholarly communication0.0130.008
Open science0.0040.015
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0160.002

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.

Opus teacher head0.768
GPT teacher head0.726
Teacher spread0.043 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations5
Published2008
Admission routes3
Has abstractyes

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