Login

Join for Free!
117454 members
table of contents table of contents

There are close parallels between Holland's general typology of careers, and the …


Biology Articles » Careers » Mapping medical careers: Questionnaire assessment of career preferences in medical school applicants and final-year students » Methods

Methods
- Mapping medical careers: Questionnaire assessment of career preferences in medical school applicants and final-year students

Two different types of data have been used in the present study. The bulk of the analysis looks at data collected during studies of medical student selection and training, and can be used to map medical careers. A subsidiary, but important, analysis looks at a large convenience sample of people of different ages who were taking part in a survey about careers in general, and were asked about their interest in a range of careers, of which only a few were medical. These latter data allowed us both to calibrate specific medical careers in the context of the general Holland typology, and to validate the INDSCAL methodology for deriving a map of careers.

Medical student data

The data were collected during three longitudinal studies of medical student selection, the first of which began in the autumn of 1980, looking at students who had applied for entry to medical school in 1981 [46,47], the second began in the autumn of 1985, studying applicants for entry to medical school in 1986 [48,49], and the third began in 1990, studying applicants for entry to medical school in 1991 [50,51]. The 1981 and 1986 cohort studies were restricted to students applying for entry to St. Mary's Hospital Medical School in London, although since applicants had each applied to five or six medical schools, many students entered schools other than St. Mary's. The 1991 cohort study looked at applicants to five different English medical schools, and because each applicant applied to several schools, these applicants represented 70% of all applicants and entrants to UK medical schools in that year. In each survey, applicants were sent questionnaires as soon as possible after UCCA, the central universities admission system, had received their application and entered their names had been onto the computer database. In general this was many weeks or even months before applicants were asked to come for interview, or were sent decisions on whether they had been accepted or rejected. The data are therefore to a great extent properly prospective.

For the present paper, the analysis of the 1981 and 1986 cohort data considers data on all applicants who replied to our questionnaires, whereas the 1991 cohort, which was very much larger, considers only those questionnaire respondents who entered medical school (although we will generally refer to this group as 'applicants' since that reflects the time at which the questionnaire was completed). In 1 we present separate information for the entire 1991 cohort which shows that there are unlikely to be response biasses, either due to differences between accepted and rejected applicants, or due to not all entrants responding to the final-year questionnaire. Response rates in the 1981, 1986 and 1991 applicants surveys were 85%, 93% and 93% [46,48,50].

Additional File 1. Additional analyses of data

Format: PDF Size: 75KB Download file

This file can be viewed with: Adobe Acrobat Reader

Students who entered medical schools in 1981, 1986 or 1991 (or in a few cases due to deferred or repeated entry, in 1982, 1987 or 1992) were followed up as final-year students in 1986 (or 1987), in 1991 (or 1992) and 1996 (or 1997). Students still in medical school were identified through their medical schools, and questionnaires sent to those medical schools. Response rates were 65%, 50%, and 56% in the follow-up of the 1981, 1986 and 1996 cohorts of students in their final year [49,51].

The questionnaires used in the study, both at application and in the final year, were detailed, typically covering 16 sides of A4, and the results reported here concern only one of the questions asked. Career preferences were assessed by a question which used the rubric,

"Below is a detailed list of specialities in which a medical career can be pursued. Please indicate your attitude towards each speciality as a possible career. If you either know nothing about a speciality, or have no opinions about it at all, simply leave that answer blank".

A list of specialities followed, each of which was rated on a five-point scale, for which the categories were, "Definite intention to go into this", "Very attractive", "Moderately attractive", "Not very attractive", and "Definite intention not to go into this".

The list of specialities varied a little over the different surveys, becoming slightly more extensive as the years passed. The original list was based on the questionnaire distributed as part of the Royal Commission on Medical Education of 1968 [52] (The Todd Report). The questionnaire for 1981 applicants had 24 questions. The final-year questionnaire for the 1981 applicants had 26 questions, the two new categories being "Pre-clinical teaching" and "Geriatric Medicine". The questionnaire for the 1986 applicants was the same as that for the 1981 applicants except that it had 25 specialities, "Geriatric medicine" having been added. The final-year questionnaire for the 1986 cohort had 27 questions, the 25 used for applicants, with the addition of "Genito-Urinary Medicine" and "Infectious Diseases". The applicant questionnaire for the 1991 cohort had the same 27 questions as did the final-year questionnaire for the 1986 cohort. The final-year questionnaire for the 1991 cohort was similar to that for the applicants except that it had 28 questions, "Radiology/Radiotherapy" having been split into two separate specialities. In the present study all of the questionnaires have been used in the form in which they were originally administered, the only omission being the speciality "Pre-clinical teaching", which was used in one survey only and is of little interest.

The general population sample

This questionnaire was completed by a sample of 1026 subjects, stratified by age using a median split (≥ 42; <42) and by sex. It asked about the suitability of twenty-four different careers for the person. Twenty careers were derived, as far as possible, from Holland's RIASEC classification, with at least three in each of the six categories. In addition there were four categories which were medical (Anaesthetist, Hospital Doctor, Psychiatrist and Surgeon). The rubric was,

"Below is a list of careers. Please indicate for each one how much you think it might have been suitable for you as a career".

Each career was rated on a five-point scale, ranging from 'Extremely suitable', through 'Very suitable' and 'Quite suitable', to 'Not very suitable' and 'Completely unsuitable'. The subjects were a convenience sample obtained from amongst friends and relations by a first year lab class at University College London, each student being responsible for obtaining a group of twelve subjects, stratified by age and sex.

Statistical analysis

INDSCAL analysis was carried out using the ALSCAL program within SPSS 10.1. Data in each subset were broken down into four groups by age and sex, age referring to mature vs non-mature students in the medical student samples (≤ 21; >21) and to subjects aged <42 or ≥ 42 in the general population sample.

The raw data, which were collected on a 5 point Likert scale, were transformed into Euclideanbased dissimilarities for all combinations of career pairs, using the PROXIMITIES program in SPSS. Four different dissimilarity matrices were produced (nonmature males, nonmature females, mature males, and mature females), and these matrices provided the basis for the INDSCAL analysis that involved minimisation, in Euclidean space, of the discrepancies between the career dissimilarities and the corresponding interpoint distances on the map. The loadings of each career on the two extracted dimensions were then plotted onto the figures to provide the maps.

The dimensionality of MDS/INDSCAL analyses can be assessed, in a manner analogous to that used in factor analysis in which eigenvalues are plotted against components. In MDS one plots a measure of 'stress' (in effect, the opposite of goodness-of-fit) against the number of dimensions which have been extracted. If too few dimensions have been extracted then the stress is high, the model not accounting adequately for the richness of the data. The optimal number of dimensions is typically indicated by a sudden 'dog-leg' in the stress plot.


rating: 4.33 from 3 votes | updated on: 9 Feb 2009 | views: 7810 |

Rate article:







excellent!bad…