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Record W6996063784

Profili motivazionali, identita e status

2006· book-chapter· en· W6996063784 on OpenAlexaboutno aff

Bibliographic record

VenueInstitutional Research Information System (Università degli Studi di Brescia) · 2006
Typebook-chapter
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsPassionQuarter (Canadian coin)TypologyEliteIdentity (music)Closure (psychology)General practiceSample (material)
DOInot available

Abstract

fetched live from OpenAlex

Three were the main questions of this paper. First, how many ways of being a general practitioner are there in Italy and can they be generalized as to make a typology of general practitioners? Second, do physicians believe that they have a common identity? Third, can the elite segment of general practitioners be analyzed as a Weberian status group?
\nThe research involved a survey (taken in 2004) on 1.162 general practitioners. The sample was stratified by sex, age and region.
\nSumming up, three kinds of general practitioners have been identified: physicians by chance, by passion and by profession. Physicians by passion are more frequently women and young; physicians by profession are more evidently men, bourgeois, older and, above all, doctor’s children; the picture of the physicians by chance is more fuzzy and blurred. A little more than a quarter of our sample said that doctors do not share a basic identity; those who actually think it but do not declare it openly are probably many more. As to the third question, self recruitment rate is about 10% and is higher among the youngest and the oldest, while a 32% comes from the upper class. Moving from ascribed factors to acquisitive dimensions, an increasingly process of self-conscious identity as well as of social closure is under way. Our research allows us to reach such conclusions only from matrimonial and sociability choices. This is more evident in that third of doctors who marry university graduates who belong to the same social class. It is at the crossroads of this segment -made up of doctors who marry a colleague and have a father physician- that the conditions for a status group are more likely to be found. In questo capitolo l’autore si pone, essenzialmente, tre interrogativi/obiettivi fra di loro intrecciati. Il primo riguarda la possibilità di individuare quanti e quali modi di essere medico di medicina generale esistano oggi in Italia. Il secondo è relativo alla percezione diretta che i medici hanno della propria identità e alla possibilità stessa di concepire un’identità comune. Il terzo consiste nell’indagare se esistano le condizioni affinché un settore della professione si configuri come un gruppo di status weberiano. 
\nQueste le risposte emerse agli interrogativi iniziali. 1. Sono stati individuati tre tipi di medici: i medici (un po’) per caso, quelli per passione e quelli per professione. Tendono ad essere medici per passione, più frequentemente, le donne e i giovani; sono più spesso medici per professione, invece, i maschi, gli individui di origine borghese, i più anziani e, soprattutto, coloro con un padre medico. 2. Poco meno di un quarto dei nostri intervistati ha dichiarato esplicitamente che fra i medici non esiste un’identità comune, ma coloro che lo pensano senza dirlo sono, molto probabilmente, più numerosi. A questo panorama fanno eccezione i trentenni. 3. Dalla ricerca si coglie, infine, un processo di innalzamento collettivo di status e di progressiva chiusura sociale a misura che si passi dalla dimensione ascritta a quella acquisitiva. I nostri questionari ci consentono di rilevarlo particolarmente in quel terzo dei medici che sposa persone laureate e della stessa classe: è nell’incrocio di quest’area con quelle, in parte coincidenti, dei medici che sposano un/a collega e di coloro che hanno un padre medico, che è più probabile si creino le condizioni di un gruppo di status.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.872
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.003
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.106
GPT teacher head0.338
Teacher spread0.232 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2006
Admission routes1
Has abstractyes

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